diff --git a/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/composition.parquet b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/composition.parquet
new file mode 100644
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--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/composition.parquet
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+size 157182
diff --git a/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/meta.csv b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/meta.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ce26148ba0582a669a31435b370deebfaacdb635
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/meta.csv
@@ -0,0 +1,205 @@
+set,sample_id,donor_id,group,age,sex,infection_source,site,sex_flag,case,paired_test_d0,donor_trained_d0,paired_test_d1,donor_trained_d1,paired_test_d2,donor_trained_d2,paired_test_d3,donor_trained_d3,paired_test_d4,donor_trained_d4
+paired,Abd-PS04,Abd-PS04,ped_sepsis,7.0,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS05,Abd-PS05,ped_sepsis,3.0,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS07,Abd-PS07,ped_sepsis,9.0,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS19,Abd-PS19,ped_sepsis,6.0,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS20,Abd-PS20,ped_sepsis,0.8,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS23,Abd-PS23,ped_sepsis,2.0,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-PS27,Abd-PS27,ped_sepsis,0.1,Male,Abdominal,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S102,Abd-S102,adult_sepsis,43.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,False,True
+paired,Abd-S103,Abd-S103,adult_sepsis,61.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S120,Abd-S120,adult_sepsis,81.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S123,Abd-S123,adult_sepsis,77.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S156,Abd-S156,adult_sepsis,72.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S39,Abd-S39,adult_sepsis,40.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S42,Abd-S42,adult_sepsis,49.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S47,Abd-S47,adult_sepsis,65.0,Female,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S51,Abd-S51,adult_sepsis,56.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S54,Abd-S54,adult_sepsis,70.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S57,Abd-S57,adult_sepsis,57.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,False,True,True,False
+paired,Abd-S66,Abd-S66,adult_sepsis,56.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,True,True,False,True,False,True,False,True,False
+paired,Abd-S79,Abd-S79,adult_sepsis,19.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S83,Abd-S83,adult_sepsis,74.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Abd-S98,Abd-S98,adult_sepsis,70.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,HC01,HC01,adult_HC,57.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,HC05,HC05,adult_HC,69.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,HC07,HC07,adult_HC,66.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,False,True,True,False,True,False,True,False,True,False
+paired,HC09,HC09,adult_HC,64.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,HC12,HC12,adult_HC,41.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,False,True,True,False,True,False,True,False
+paired,HC13,HC13,adult_HC,55.0,Female,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,HC16,HC16,adult_HC,72.0,Female,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC10,IC10,adult_ICU_ctrl,40.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC15,IC15,adult_ICU_ctrl,48.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC16,IC16,adult_ICU_ctrl,71.0,Female,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC19,IC19,adult_ICU_ctrl,51.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC22,IC22,adult_ICU_ctrl,68.0,Male,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,IC23,IC23,adult_ICU_ctrl,54.0,Female,none,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,False,True
+paired,PHC01,PHC01,ped_HC,9.0,Female,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,PHC02,PHC02,ped_HC,6.0,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,PHC03,PHC03,ped_HC,2.0,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,PHC05,PHC05,ped_HC,5.0,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,False,True,True,False
+paired,PIC01,PIC01,ped_ICU_ctrl,0.1,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,False,True,True,False,True,False
+paired,PIC02,PIC02,ped_ICU_ctrl,9.0,Female,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,PIC04,PIC04,ped_ICU_ctrl,7.0,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,PIC05,PIC05,ped_ICU_ctrl,5.0,Male,none,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,False,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS02,Res-PS02,ped_sepsis,15.0,Male,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS03,Res-PS03,ped_sepsis,1.0,Female,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS12,Res-PS12,ped_sepsis,1.0,Female,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS16,Res-PS16,ped_sepsis,3.0,Male,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS21,Res-PS21,ped_sepsis,13.0,Male,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS22,Res-PS22,ped_sepsis,6.0,Male,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,False,True
+paired,Res-PS25,Res-PS25,ped_sepsis,0.3,Male,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-PS26,Res-PS26,ped_sepsis,5.0,Female,Respiratory,"Children's Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S10,Res-S10,adult_sepsis,65.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S125,Res-S125,adult_sepsis,59.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S127,Res-S127,adult_sepsis,77.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S136,Res-S136,adult_sepsis,46.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,False,True,True,False,True,False
+paired,Res-S139,Res-S139,adult_sepsis,73.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,False,True,True,False
+paired,Res-S146,Res-S146,adult_sepsis,75.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S149,Res-S149,adult_sepsis,81.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S150,Res-S150,adult_sepsis,83.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,True,True,False,True,False,True,False,True,False
+paired,Res-S164,Res-S164,adult_sepsis,79.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,False,True,True,False,True,False,True,False
+paired,Res-S166,Res-S166,adult_sepsis,56.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,False,True,True,False,True,False,True,False
+paired,Res-S174,Res-S174,adult_sepsis,40.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,False,True,True,False
+paired,Res-S177,Res-S177,adult_sepsis,40.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,False,True,True,False,True,False
+paired,Res-S184,Res-S184,adult_sepsis,82.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S19,Res-S19,adult_sepsis,44.0,Female,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S26,Res-S26,adult_sepsis,58.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S40,Res-S40,adult_sepsis,81.0,Male,Respiratory,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Res-S77,Res-S77,adult_sepsis,35.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,True,True,False,False,True,True,False,True,False
+paired,Res-S87,Res-S87,adult_sepsis,70.0,Male,Respiratory,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S140,Skin-S140,adult_sepsis,42.0,Male,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S142,Skin-S142,adult_sepsis,71.0,Male,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,False,True,False,True
+paired,Skin-S152,Skin-S152,adult_sepsis,69.0,Male,Skin,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S38,Skin-S38,adult_sepsis,39.0,Male,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S43,Skin-S43,adult_sepsis,46.0,Female,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S46,Skin-S46,adult_sepsis,73.0,Female,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,False,True
+paired,Skin-S52,Skin-S52,adult_sepsis,54.0,Female,Skin,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Skin-S74,Skin-S74,adult_sepsis,60.0,Male,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,True,True,False,False,True,True,False,True,False
+paired,Uri-S134,Uri-S134,adult_sepsis,67.0,Male,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,True,False,False,True,True,False,True,False,True,False
+paired,Uri-S147,Uri-S147,adult_sepsis,68.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,False,True,True,False,True,False,True,False
+paired,Uri-S157,Uri-S157,adult_sepsis,74.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Uri-S20,Uri-S20,adult_sepsis,58.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+paired,Uri-S93,Uri-S93,adult_sepsis,53.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,True,False,True,False,True,False,True,False,True,False
+bulkonly,Abd-S114,Abd-S114,adult_sepsis,84.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S115,Abd-S115,adult_sepsis,83.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S126,Abd-S126,adult_sepsis,82.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S128,Abd-S128,adult_sepsis,55.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S132,Abd-S132,adult_sepsis,91.0,Female,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S133,Abd-S133,adult_sepsis,45.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S137,Abd-S137,adult_sepsis,30.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S141,Abd-S141,adult_sepsis,82.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S148,Abd-S148,adult_sepsis,82.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S159,Abd-S159,adult_sepsis,76.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S165,Abd-S165,adult_sepsis,59.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S185,Abd-S185,adult_sepsis,81.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S190,Abd-S190,adult_sepsis,74.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S197,Abd-S197,adult_sepsis,71.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S198,Abd-S198,adult_sepsis,78.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S24,Abd-S24,adult_sepsis,38.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S25,Abd-S25,adult_sepsis,44.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S35,Abd-S35,adult_sepsis,48.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S37,Abd-S37,adult_sepsis,46.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S92,Abd-S92,adult_sepsis,82.0,Male,Abdominal,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S96,Abd-S96,adult_sepsis,76.0,Male,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Abd-S97,Abd-S97,adult_sepsis,47.0,Female,Abdominal,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
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+bulkonly,Skin-S192,Skin-S192,adult_sepsis,40.0,Female,Skin,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
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+bulkonly,Uri-S104,Uri-S104,adult_sepsis,63.0,Female,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S124,Uri-S124,adult_sepsis,88.0,Male,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S143,Uri-S143,adult_sepsis,54.0,Female,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S155,Uri-S155,adult_sepsis,73.0,Male,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S162,Uri-S162,adult_sepsis,58.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S180,Uri-S180,adult_sepsis,64.0,Male,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S200,Uri-S200,adult_sepsis,76.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S201,Uri-S201,adult_sepsis,29.0,Male,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S208,Uri-S208,adult_sepsis,70.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S213,Uri-S213,adult_sepsis,74.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S217,Uri-S217,adult_sepsis,73.0,Female,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S48,Uri-S48,adult_sepsis,53.0,Male,Urinary,"the First Affiliated Hospital of Chongqing Medical University, Chongqing, China",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S94,Uri-S94,adult_sepsis,68.0,Male,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
+bulkonly,Uri-S95,Uri-S95,adult_sepsis,72.0,Female,Urinary,"Chongqing Emergency Medical Center, Chongqing, China.",False,True,False,False,False,False,False,False,False,False,False,False
diff --git a/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/states.parquet b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/states.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..78c67d3bf1a7664588a50085f3f707bfc65cb703
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/features/ye2026_sepsis/states.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1a48305d7b56764852c28c3bc66f5323d3e4f7c307901772e98301b92263a28
+size 165376
diff --git a/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/wf/findings_imperial_eskd.json b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/wf/findings_imperial_eskd.json
new file mode 100644
index 0000000000000000000000000000000000000000..1b7e312f2b9c2c67900ca83648a3d4b2b2039daf
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261006T033406Z-bulkonly-lora5-k1000/diffusion/dataset/server_1/model8/plans/bulkonly_lora5_20261006/analysis/wf/findings_imperial_eskd.json
@@ -0,0 +1,6272 @@
+{
+ "cohort": "imperial_eskd",
+ "date": "2026-10-06",
+ "report": "Returned as text in the synthesis agent's final response: subagents may not write report files, so /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/REPORT_imperial_eskd.md is saved by the coordinator.",
+ "grading_rule": "refuted if any verifier refuted; confirmed only if both confirmed; else qualified",
+ "counts": {
+  "confirmed": 0,
+  "qualified": 19,
+  "refuted": 2
+ },
+ "cross_cutting_conclusions": [
+  "No finding is confirmed by both verifiers: 19 are qualified and 2 refuted (IMP-CLIN-4, IMP-LONG-2). IMP-LONG-1 (ISG-high monocyte decline over days) received one 'confirmed' verdict.",
+  "In 164 bulk-only samples (76 donors, 45 without any single cells) the generated cells reproduce, in direction, the known COVID/ESKD immune shifts: an interferon response in every lineage that wanes over the first weeks; monocyte HLA class II loss and an S100/CD163 programme with severity; more B-lineage and plasma cells; more platelets with severity. All are in Stephenson 2025, Gisby 2022 or other COVID cohorts, and all are carried by the bulk.",
+  "Wave is the main trap: 88% of bulk-only COVID+ samples are wave 1, but half of the paired truth is wave 2 (different single-cell processing site and thaw protocol, dexamethasone era). Several observed contrasts exist only in wave 2; recalibrating on wave 1 changes which draws carry results (synthesis_imperial_eskd/i01_codes_by_feature.csv).",
+  "Fresh-PBMC bulk vs cryopreserved single cells: the bulk carries LDG/neutrophil and S100 alarmin RNA that the single cells lack. The generator turns it into extra classical monocytes and fewer T cells (IMP-CLIN-5), which inflates or creates monocyte-state, T-subset and platelet associations (IMP-POP-3, IMP-PROG-3, IMP-POP-2 draw 4, IMP-CLIN-3, IMP-LONG-5, IMP-CLIN-4).",
+  "Label artefacts tied to interferon: AIFI 'naive B' absorbs ISG-high memory B (IMP-LONG-2), 'Proliferating NK' is mostly ISG-high non-cycling NK (IMP-LONG-3), and 'CD8aa' marks CD8B+ memory-like cells (IMP-CLIN-4).",
+  "Fatal outcome adds nothing beyond peak severity in any finding; the paired fatal truth is 7 of 8 donors from wave 2, and fatal labels disagree between data sources.",
+  "Beyond the bulk: only the COVID+ vs COVID- double-centred cell-type pattern (IMP-PROG-2) survives a donor-disjoint, wave-matched test, at about half the sepsis strength (4-8% of reliable variance). This replicates SEP-PROG-4 for disease vs control only.",
+  "No new population survives. Artefacts: a generated-only mixed-lineage 'T' cluster, over-produced DN T, a spurious CD14 expansion.",
+  "Trust-layer defect: calibration.csv's severity contrast selects on severity_peak alone, so all 10 paired recovery samples (8 of them from mild/moderate donors) sit in the severe arm. Restricted to COVID+ the observed severity contrast becomes informative for 13-18 features per draw instead of 4-12."
+ ],
+ "generated": {
+  "samples": [
+   {
+    "set": "bulkonly",
+    "group": "covid_negative",
+    "n_samples": 19,
+    "n_donors": 19
+   },
+   {
+    "set": "bulkonly",
+    "group": "covid_positive",
+    "n_samples": 143,
+    "n_donors": 58
+   },
+   {
+    "set": "bulkonly",
+    "group": "recovery",
+    "n_samples": 2,
+    "n_donors": 2
+   },
+   {
+    "set": "paired",
+    "group": "covid_negative",
+    "n_samples": 34,
+    "n_donors": 34
+   },
+   {
+    "set": "paired",
+    "group": "covid_positive",
+    "n_samples": 113,
+    "n_donors": 35
+   },
+   {
+    "set": "paired",
+    "group": "recovery",
+    "n_samples": 10,
+    "n_donors": 10
+   }
+  ],
+  "cells": [
+   {
+    "set": "observed",
+    "draw": -1,
+    "n_samples": 157,
+    "n_cells": 514260,
+    "cells_per_sample_min": 226,
+    "cells_per_sample_median": 2483.0,
+    "cells_per_sample_max": 11388
+   },
+   {
+    "set": "paired_gen",
+    "draw": 0,
+    "n_samples": 142,
+    "n_cells": 213000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "bulkonly",
+    "draw": 0,
+    "n_samples": 164,
+    "n_cells": 246000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "paired_gen",
+    "draw": 1,
+    "n_samples": 143,
+    "n_cells": 214500,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "bulkonly",
+    "draw": 1,
+    "n_samples": 164,
+    "n_cells": 246000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "paired_gen",
+    "draw": 2,
+    "n_samples": 142,
+    "n_cells": 213000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "bulkonly",
+    "draw": 2,
+    "n_samples": 164,
+    "n_cells": 246000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "paired_gen",
+    "draw": 3,
+    "n_samples": 145,
+    "n_cells": 217500,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "bulkonly",
+    "draw": 3,
+    "n_samples": 164,
+    "n_cells": 246000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "paired_gen",
+    "draw": 4,
+    "n_samples": 143,
+    "n_cells": 214500,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   },
+   {
+    "set": "bulkonly",
+    "draw": 4,
+    "n_samples": 164,
+    "n_cells": 246000,
+    "cells_per_sample_min": 1500,
+    "cells_per_sample_median": 1500.0,
+    "cells_per_sample_max": 1500
+   }
+  ],
+  "training_donors": [
+   {
+    "draw": 0,
+    "train_donors": "C106;C237;C126;C141;C58",
+    "profile": "C106:covid_negative:none:fatal=nan:w1:n=1 ; C237:covid_negative:none:fatal=nan:w1:n=1 ; C126:covid_positive:mild/moderate:fatal=False:w1:n=3 ; C141:covid_positive:severe/critical:fatal=True:w2:n=4 ; C58:covid_negative/covid_positive/recovery:mild/moderate/none/severe/critical:fatal=False/nan:w1|w2/w2:n=6"
+   },
+   {
+    "draw": 1,
+    "train_donors": "C142;C164;C65;C31;C101",
+    "profile": "C142:covid_negative:none:fatal=nan:w1:n=1 ; C164:covid_negative:none:fatal=nan:w1:n=1 ; C65:covid_positive:severe/critical:fatal=False:w1:n=3 ; C31:covid_positive:mild/moderate:fatal=False:w1:n=3 ; C101:covid_positive/recovery:severe/critical:fatal=True:w2:n=6"
+   },
+   {
+    "draw": 2,
+    "train_donors": "C164;C85;C101;C60;C187",
+    "profile": "C164:covid_negative:none:fatal=nan:w1:n=1 ; C85:covid_negative:none:fatal=nan:w1:n=1 ; C101:covid_positive/recovery:severe/critical:fatal=True:w2:n=6 ; C60:covid_positive:severe/critical:fatal=False:w1:n=1 ; C187:covid_negative/covid_positive/recovery:mild/moderate/none/severe/critical:fatal=False/nan:w1|w2/w2:n=6"
+   },
+   {
+    "draw": 3,
+    "train_donors": "C240;C251;C31;C108;C40",
+    "profile": "C240:covid_negative:none:fatal=nan:w1:n=1 ; C251:covid_negative:none:fatal=nan:w1:n=1 ; C31:covid_positive:mild/moderate:fatal=False:w1:n=3 ; C108:covid_negative/covid_positive:none/severe/critical:fatal=True/nan:w1|w2/w2:n=4 ; C40:covid_positive:severe/critical:fatal=False:w1:n=3"
+   },
+   {
+    "draw": 4,
+    "train_donors": "C152;C137;C33;C170;C36",
+    "profile": "C152:covid_negative:none:fatal=nan:w1:n=1 ; C137:covid_negative/recovery:none/severe/critical:fatal=False/nan:w1|w2/w2:n=2 ; C33:covid_positive:severe/critical:fatal=True:w2:n=3 ; C170:covid_negative/covid_positive/recovery:mild/moderate/none/severe/critical:fatal=False/nan:w1|w2/w2:n=5 ; C36:covid_positive:mild/moderate:fatal=False:w1:n=3"
+   }
+  ],
+  "paired_test": [
+   {
+    "draw": 0,
+    "n_test": 142,
+    "n_test_donors": 52,
+    "covid_negative": 31,
+    "covid_positive": 102,
+    "recovery": 9,
+    "wave_w1": 67,
+    "wave_w1|w2": 12,
+    "wave_w2": 63,
+    "covpos_mild/moderate": 62,
+    "covpos_severe/critical": 40,
+    "covpos_fatal_donors": 7
+   },
+   {
+    "draw": 1,
+    "n_test": 143,
+    "n_test_donors": 52,
+    "covid_negative": 32,
+    "covid_positive": 102,
+    "recovery": 9,
+    "wave_w1": 64,
+    "wave_w1|w2": 13,
+    "wave_w2": 66,
+    "covpos_mild/moderate": 66,
+    "covpos_severe/critical": 36,
+    "covpos_fatal_donors": 7
+   },
+   {
+    "draw": 2,
+    "n_test": 142,
+    "n_test_donors": 52,
+    "covid_negative": 31,
+    "covid_positive": 103,
+    "recovery": 8,
+    "wave_w1": 69,
+    "wave_w1|w2": 12,
+    "wave_w2": 61,
+    "covpos_mild/moderate": 65,
+    "covpos_severe/critical": 38,
+    "covpos_fatal_donors": 7
+   },
+   {
+    "draw": 3,
+    "n_test": 145,
+    "n_test_donors": 52,
+    "covid_negative": 31,
+    "covid_positive": 104,
+    "recovery": 10,
+    "wave_w1": 64,
+    "wave_w1|w2": 12,
+    "wave_w2": 69,
+    "covpos_mild/moderate": 66,
+    "covpos_severe/critical": 38,
+    "covpos_fatal_donors": 7
+   },
+   {
+    "draw": 4,
+    "n_test": 143,
+    "n_test_donors": 52,
+    "covid_negative": 31,
+    "covid_positive": 104,
+    "recovery": 8,
+    "wave_w1": 68,
+    "wave_w1|w2": 11,
+    "wave_w2": 64,
+    "covpos_mild/moderate": 63,
+    "covpos_severe/critical": 41,
+    "covpos_fatal_donors": 7
+   }
+  ],
+  "sources": [
+   "synthesis_imperial_eskd/i00_counts.txt",
+   "synthesis_imperial_eskd/i00_samples_by_set_group.csv",
+   "synthesis_imperial_eskd/i00_cells_by_set_draw.csv",
+   "synthesis_imperial_eskd/i00_training_donors.csv",
+   "synthesis_imperial_eskd/i00_paired_test_by_draw.csv"
+  ]
+ },
+ "calibration_summary": {
+  "gates": "level: 0.5<=ratio<=2 and Spearman>=0.3; PASS = informative observed contrast (donor-level MWU p<0.10) + level + same sign + gen/obs contrast in [0.5,2]; DIR = informative + 1/3<=ratio<=3, Spearman>=0.2, same sign; NULL = observed p>=0.10 (NULL_genfx if generated p<0.05); else FAIL",
+  "restrictions": {
+   "R0_official": "calibration.csv as published (reproduced to <=5e-6)",
+   "R1_covid_pos": "severity contrast and level over COVID+ test samples only",
+   "R2_wave1": "wave-1 test samples only"
+  },
+  "per_draw": [
+   {
+    "restriction": "R0_official",
+    "draw": 0,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 21,
+    "spearman_ge_0p3": 26,
+    "ratio_0p5_2": 33,
+    "same_sign": 43,
+    "obs_informative": 23,
+    "PASS": 10,
+    "DIR": 5,
+    "FAIL": 8,
+    "NULL": 15,
+    "NULL_genfx": 12,
+    "NA": 0,
+    "PASS_composition": 7,
+    "PASSorDIR_composition": 12,
+    "PASS_state": 3,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 0,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 21,
+    "spearman_ge_0p3": 26,
+    "ratio_0p5_2": 33,
+    "same_sign": 34,
+    "obs_informative": 6,
+    "PASS": 3,
+    "DIR": 1,
+    "FAIL": 2,
+    "NULL": 38,
+    "NULL_genfx": 6,
+    "NA": 0,
+    "PASS_composition": 1,
+    "PASSorDIR_composition": 2,
+    "PASS_state": 2,
+    "PASSorDIR_state": 2
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 1,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 24,
+    "spearman_ge_0p3": 31,
+    "ratio_0p5_2": 33,
+    "same_sign": 45,
+    "obs_informative": 24,
+    "PASS": 7,
+    "DIR": 9,
+    "FAIL": 8,
+    "NULL": 18,
+    "NULL_genfx": 8,
+    "NA": 0,
+    "PASS_composition": 2,
+    "PASSorDIR_composition": 10,
+    "PASS_state": 5,
+    "PASSorDIR_state": 6
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 1,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 24,
+    "spearman_ge_0p3": 31,
+    "ratio_0p5_2": 33,
+    "same_sign": 38,
+    "obs_informative": 4,
+    "PASS": 2,
+    "DIR": 1,
+    "FAIL": 1,
+    "NULL": 42,
+    "NULL_genfx": 4,
+    "NA": 0,
+    "PASS_composition": 2,
+    "PASSorDIR_composition": 3,
+    "PASS_state": 0,
+    "PASSorDIR_state": 0
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 2,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 30,
+    "ratio_0p5_2": 38,
+    "same_sign": 40,
+    "obs_informative": 24,
+    "PASS": 8,
+    "DIR": 6,
+    "FAIL": 10,
+    "NULL": 8,
+    "NULL_genfx": 18,
+    "NA": 0,
+    "PASS_composition": 4,
+    "PASSorDIR_composition": 9,
+    "PASS_state": 4,
+    "PASSorDIR_state": 5
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 2,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 30,
+    "ratio_0p5_2": 38,
+    "same_sign": 38,
+    "obs_informative": 12,
+    "PASS": 8,
+    "DIR": 2,
+    "FAIL": 2,
+    "NULL": 35,
+    "NULL_genfx": 3,
+    "NA": 0,
+    "PASS_composition": 6,
+    "PASSorDIR_composition": 8,
+    "PASS_state": 2,
+    "PASSorDIR_state": 2
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 3,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 32,
+    "ratio_0p5_2": 35,
+    "same_sign": 39,
+    "obs_informative": 23,
+    "PASS": 7,
+    "DIR": 8,
+    "FAIL": 8,
+    "NULL": 17,
+    "NULL_genfx": 10,
+    "NA": 0,
+    "PASS_composition": 5,
+    "PASSorDIR_composition": 11,
+    "PASS_state": 2,
+    "PASSorDIR_state": 4
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 3,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 32,
+    "ratio_0p5_2": 35,
+    "same_sign": 37,
+    "obs_informative": 5,
+    "PASS": 1,
+    "DIR": 2,
+    "FAIL": 2,
+    "NULL": 43,
+    "NULL_genfx": 2,
+    "NA": 0,
+    "PASS_composition": 1,
+    "PASSorDIR_composition": 3,
+    "PASS_state": 0,
+    "PASSorDIR_state": 0
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 4,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 16,
+    "spearman_ge_0p3": 25,
+    "ratio_0p5_2": 32,
+    "same_sign": 47,
+    "obs_informative": 22,
+    "PASS": 7,
+    "DIR": 9,
+    "FAIL": 6,
+    "NULL": 13,
+    "NULL_genfx": 15,
+    "NA": 0,
+    "PASS_composition": 4,
+    "PASSorDIR_composition": 9,
+    "PASS_state": 3,
+    "PASSorDIR_state": 7
+   },
+   {
+    "restriction": "R0_official",
+    "draw": 4,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 16,
+    "spearman_ge_0p3": 25,
+    "ratio_0p5_2": 32,
+    "same_sign": 40,
+    "obs_informative": 9,
+    "PASS": 2,
+    "DIR": 5,
+    "FAIL": 2,
+    "NULL": 36,
+    "NULL_genfx": 5,
+    "NA": 0,
+    "PASS_composition": 1,
+    "PASSorDIR_composition": 4,
+    "PASS_state": 1,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 0,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 21,
+    "spearman_ge_0p3": 26,
+    "ratio_0p5_2": 33,
+    "same_sign": 43,
+    "obs_informative": 23,
+    "PASS": 10,
+    "DIR": 5,
+    "FAIL": 8,
+    "NULL": 15,
+    "NULL_genfx": 12,
+    "NA": 0,
+    "PASS_composition": 7,
+    "PASSorDIR_composition": 12,
+    "PASS_state": 3,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 0,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 17,
+    "spearman_ge_0p3": 25,
+    "ratio_0p5_2": 32,
+    "same_sign": 37,
+    "obs_informative": 16,
+    "PASS": 2,
+    "DIR": 4,
+    "FAIL": 10,
+    "NULL": 28,
+    "NULL_genfx": 6,
+    "NA": 0,
+    "PASS_composition": 0,
+    "PASSorDIR_composition": 3,
+    "PASS_state": 2,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 1,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 24,
+    "spearman_ge_0p3": 31,
+    "ratio_0p5_2": 33,
+    "same_sign": 45,
+    "obs_informative": 24,
+    "PASS": 7,
+    "DIR": 9,
+    "FAIL": 8,
+    "NULL": 18,
+    "NULL_genfx": 8,
+    "NA": 0,
+    "PASS_composition": 2,
+    "PASSorDIR_composition": 10,
+    "PASS_state": 5,
+    "PASSorDIR_state": 6
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 1,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 19,
+    "spearman_ge_0p3": 25,
+    "ratio_0p5_2": 33,
+    "same_sign": 39,
+    "obs_informative": 15,
+    "PASS": 4,
+    "DIR": 4,
+    "FAIL": 7,
+    "NULL": 26,
+    "NULL_genfx": 9,
+    "NA": 0,
+    "PASS_composition": 2,
+    "PASSorDIR_composition": 5,
+    "PASS_state": 2,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 2,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 30,
+    "ratio_0p5_2": 38,
+    "same_sign": 40,
+    "obs_informative": 24,
+    "PASS": 8,
+    "DIR": 6,
+    "FAIL": 10,
+    "NULL": 8,
+    "NULL_genfx": 18,
+    "NA": 0,
+    "PASS_composition": 4,
+    "PASSorDIR_composition": 9,
+    "PASS_state": 4,
+    "PASSorDIR_state": 5
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 2,
+    "contrast": "severe/critical vs mild/moderate (COVID+)",
+    "n_features": 50,
+    "level_strict": 22,
+    "spearman_ge_0p3": 29,
+    "ratio_0p5_2": 37,
+    "same_sign": 37,
+    "obs_informative": 18,
+    "PASS": 7,
+    "DIR": 4,
+    "FAIL": 7,
+    "NULL": 22,
+    "NULL_genfx": 10,
+    "NA": 0,
+    "PASS_composition": 5,
+    "PASSorDIR_composition": 8,
+    "PASS_state": 2,
+    "PASSorDIR_state": 3
+   },
+   {
+    "restriction": "R1_covid_pos",
+    "draw": 3,
+    "contrast": "COVID+ vs COVID-",
+    "n_features": 50,
+    "level_strict": 25,
+    "spearman_ge_0p3": 32,
+    "ratio_0p5_2": 35,
+    "same_sign": 39,
+    "obs_informative": 23,
+    "PASS": 7,
+    "DIR": 8,
+    "FAIL": 8,
+    "NULL": 17,
+    "NULL_genfx": 10,
+    "NA": 0,
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+    "PASSorDIR_composition": 11,
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+    "q_by_draw": "0.4 0.006 0.06 0.3 0.9",
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+    "feature": "l2 | Proliferating NK cell",
+    "coef_by_draw": "+0.03 +0.37 +0.74 +0.24 -0.05",
+    "q_by_draw": "0.9 0.7 0.6 0.8 0.9",
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+   }
+  ],
+  "source": "synthesis_imperial_eskd/i03_dc_nk_clinical_screen.csv (from imp_clinical/s05_summary.csv)"
+ },
+ "findings": [
+  {
+   "id": "IMP-PROG-4",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 1,
+    "theme": "interferon (positive control)",
+    "synthesis_strength": "strong as a COVID+ vs COVID- positive control; nothing beyond bulk; two sub-claims refuted",
+    "reportable_statement": "In bulk-only patients the generated cells raise the interferon-stimulated (ISG) programme in every testable major lineage for COVID+ vs COVID- (CD14 monocytes +0.96 to +1.32 log2; draw 0 7.08 vs 5.84) and SIGLEC1 in the three monocyte types, as observed paired cells do. The bulk ISG score already carries it. The claimed ordering of the response across cell types and the claim that the severity ISG difference does not carry over are not supported.",
+    "carrying_draws": {
+     "ISG, COVID+ vs COVID-": "0-4 for CD14, CD16 and intermediate monocytes, CD56dim NK, memory CD4, memory CD8, cDC2, gdT (8 types; 4-9 depending on the score definition); >=3 draws in 13 of 15 types; MAIT and pDC none (testable in 4 and 2 draws)",
+     "SIGLEC1": "monocytes 0-4; cDC2 0,1,3,4 (Spearman 0.43-0.63)",
+     "severity, time-adjusted CD14 ISG": "0-4 (verifier analysis, not the lens claim)"
+    },
+    "honesty": {
+     "calibration": "Programme not in calibration.csv. Paired-test gate passes 5/5 in most types: ratio 0.31-1.07 (Treg lowest), CD14 per-sample Spearman 0.81-0.89. calibration.csv 'ISG-high monocyte' ratio 0.65-0.81, Spearman 0.78-0.85; 'ISG-high T' 0.29-0.60, 0.72-0.81; both contrasts same sign 5/5. Wave-1-only calibration passes for the major types.",
+     "replication": "5/5 draws in every testable type. Survives sex flag, training donors, wave 1 only, one sample per donor, donors on both sides, age/sex, per-cell depth equalisation, label confidence >=0.9 and myeloid-free T/NK cells. Diabetes and ethnicity shrink it 20-30%, as they shrink the bulk. Shared noise: ISG-high CD14 same-slot OR 39-86 vs 4.8-13.7 shifted.",
+     "observed": "Observed +0.93 (MAIT) to +2.14 (CD16 monocytes), p <= 0.004 in every type (the within-donor pre-infection to infection check is under IMP-POP-4).",
+     "beyond_bulk": "None. Bulk ISG +1.38 (p 1.6e-4) predicts observed type-level ISG better than the generated cells in all 15 types; the generated score adds to bulk ISG10 in 1 of 71 likelihood-ratio tests."
+    },
+    "verdict_reasons": [
+     "Cross-type ordering is a sample-selection plus IFI27 effect: on a common sample set the observed 9-gene response is flat (0.63-0.88; CD14 among the lowest) and bulk-only vs observed r is -0.60 to +0.44.",
+     "The within-sample ordering fails paired calibration: generated cells differ from the reference in 5-9 of 13 types, observed in 0; Treg goes the opposite way.",
+     "'Severity ISG does not carry over' is a sampling-time confound (bulk-only severe median day 15 vs 9.5 for mild). Adjusted for days since swab, CD14 severity ISG is +0.92 to +1.31 (p 0.0013-0.0075, 5/5; within 14 days +0.85 to +1.28, 4/5) and bulk ISG +1.09 (p 0.005).",
+     "MAIT and pDC cannot be tested; naive B is fragile.",
+     "Generated COVID+ lymphoid pseudobulks carry myeloid transcripts (23/49 tests vs 0/10 observed); myeloid-free T/NK cells keep the ISG rise.",
+     "The quoted '0.3-1.1x of observed' is the paired calibration ratio; the bulk-only effect is 0.18-0.73x of the observed paired effect.",
+     "Gisby 2022 PDF p.2 reports SIGLEC1 in the bulk; the IFN pathway enrichment is on p.5 and concerns the plasma proteome."
+    ],
+    "refuted_subclaims": [
+     "cell-type ordering follows the observed one",
+     "the severity ISG difference does not carry over to the bulk-only cohort"
+    ],
+    "new_vs_cohort_papers": "Known. Stephenson 2025: IFN-alpha/beta responses across innate and adaptive cells (PDF p.4); IFN-stimulated states peak in week 1, higher early in severe disease (PDF p.8-9). Gisby 2022: SIGLEC1 in bulk (p.2), monocyte SIGLEC-1 and type I IFN earlier and stronger in severe disease (pp.7-8). Non-ESKD cohorts (Combes 2021, Arunachalam 2020) report the opposite severity direction.",
+    "next_steps": [
+     "Use as a positive control only.",
+     "Report severity contrasts within a fixed window (<=14 days) or adjusted for days since swab.",
+     "Test any cell-type ordering on a common sample set against observed cells, with and without IFI27.",
+     "Filter myeloid-transcript leakage from generated lymphoid cells before lymphoid programme DE."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-4_recompute/v08_key_numbers.csv",
+     "verify/IMP-PROG-4_recompute/v01_summary.csv",
+     "verify/IMP-PROG-4_recompute/v04b_profile_correlations.csv",
+     "verify/IMP-PROG-4_recompute/v05_clean_lymphoid_tests.csv",
+     "verify/IMP-PROG-4_recompute/v07_severity_time.csv",
+     "verify/IMP-PROG-4_calib/v04_common_sample_profile_corr.csv",
+     "verify/IMP-PROG-4_calib/v02_interaction_calib_summary.csv",
+     "verify/IMP-PROG-4_calib/v03_bulk_lr.csv",
+     "verify/IMP-PROG-4_calib/v05_severity_robust.csv",
+     "imp_populations/tables/s12_shared_noise.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-4",
+    "cohort": "imperial_eskd",
+    "claim": "For COVID+ vs COVID-, the generated cells of bulk-only patients raise the interferon-stimulated programme in every major cell type and SIGLEC1 in monocytes and cDC2. The size is 0.3-1.1x of the observed paired effect, and the ordering across cell types follows the observed one. The larger ISG difference between severe and mild patients seen in paired cells does not carry over to the bulk-only cohort.",
+    "kind": "program",
+    "feature": "ISG programme (ISG15, IFI6, IFI27, IFI44L, MX1, OAS1, IFIT1, IFIT3, RSAD2, STAT1) in 15 L2 types; SIGLEC1 (Gisby 2022) in monocytes and cDC2.",
+    "comparison": "COVID+ vs COVID- (58 vs 19 donors), donor-aware. Also severe vs mild (28 vs 30) as a negative check.",
+    "effect_bulkonly": "ISG difference in log2, range over draws: CD14 +0.96 to +1.32 (draw 0 group means 7.08 vs 5.84); CD16 +0.89 to +1.13; intermediate +0.83 to +1.21; CD56dim NK +0.57 to +0.95; memory CD8 +0.90 to +1.11; memory CD4 +0.67 to +0.85; cDC2 +0.66 to +0.93; gdT +0.62 to +0.76; naive B +0.54 to +0.86; memory B +0.32 to +0.67; naive CD8 +0.23 to +0.67; naive CD4 +0.34 to +0.64; Treg +0.23 to +0.40. SIGLEC1: CD14 +0.86 to +1.59; CD16 +0.52 to +0.90; intermediate +0.64 to +1.43; cDC2 +0.45 to +0.71. Severity, CD14 ISG: +0.53 to +0.79, p 0.05-0.2 (not significant).",
+    "stats": "Donor-aware Welch test; drawwise BH q for CD14 ISG 0.002-0.015. All five draws carry the result (PASS gate and q < 0.05) in 8 of 15 types: CD14, CD16 and intermediate monocytes, CD56dim NK, memory CD4, memory CD8, cDC2 and gdT. At least 3 draws carry it in 13 of 15 types; MAIT and pDC are not carried.",
+    "draws_same_sign": "5/5 draws positive in all 15 types for COVID.",
+    "paired_calibration": "Per-draw programme calibration: PASS 5/5 in most types. Generated/observed ratio in the carrying draws 0.32-1.07 (Treg lowest). Per-sample Spearman in CD14 0.81-0.89, against 0.92-0.94 for the bulk ISG score (partial 0.07-0.22). Cross-type profile on the tests: r 0.88-0.92. calibration.csv 'ISG-high monocyte (positive control)': ratio 0.65-0.81, Spearman 0.78-0.85, COVID and severity sign 5/5. 'ISG-high T': ratio 0.29-0.60, Spearman 0.72-0.81, sign 5/5.",
+    "paired_observed_concordance": "Observed COVID ISG effects range from +0.93 (MAIT) to +2.14 (CD16 monocytes), p <= 0.004 in every type. The bulk-only per-type effects correlate with the observed ones across 14-15 types at r 0.74-0.86. But the observed within-sample difference CD14 minus naive CD4 is +0.24 (p 0.16), so the cross-type ordering is not itself significant in observed cells.",
+    "beyond_bulk": "Little. The bulk ISG score rises by +1.36 (p < 0.001) and tracks patients better, and the generated score adds nothing to it (likelihood-ratio p 0.22-0.93 in all contrasts). What the bulk cannot say is that the response appears in every lineage and is largest in monocytes, cDC2, NK and memory CD8. That pattern matches observed cells, but the ordering is only weakly supported. Severity ISG does not transfer from the paired to the bulk-only cohort: the bulk-only bulk shows +0.63 (p 0.11) against +1.51 (p 0.0003) in the paired bulk. Paired severe samples are +0.82 above bulk-only severe samples in bulk ISG. Wave 2, which was sampled systematically and early, adds +0.98 in the paired set and +1.22 in the bulk-only set. So I make no severity-ISG claim.",
+    "novelty": "Reported. Stephenson 2025, PDF p.4: IFN-alpha/beta responses are enriched across a broad range of innate and adaptive cells in COVID+. Gisby 2022, PDF p.2: IFN pathways in the bulk; PDF p.7: SIGLEC-1 on CD14 monocytes tracks the type I IFN signature.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/program_map.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/program_map_wide.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_profiles.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_interaction.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/bulk_baseline_programs.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s07_calibration_transfer.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_contrast_agreement.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_set_shift.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_wave_effect.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/group_means.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a8bd911e885ef8a5d",
+     "verdict": "qualified",
+     "recomputed": "All numbers come from my own code (CPU only, memory-capped scopes), built from the raw compact files. Units are donor x group means with a Welch test. Every test is run within each draw (d0-d4). BH is applied over 19 features (15 ISG types + 4 SIGLEC1) per draw.\n\nREPRODUCTION, using the discovery-equivalent edgeR prior-count score written independently:\n- COVID ISG effect: CD14 1.24/0.98/1.14/1.32/0.96 (d0 group means 7.08 vs 5.84; q 0.0014-0.012); CD16 0.89-1.13; Int 0.83-1.21; NK 0.57-0.95; memCD8 0.90-1.11; memCD4 0.67-0.85; cDC2 0.66-0.93; gdT 0.62-0.76; naiveB 0.54-0.86; memB 0.32-0.67; naiveCD8 0.19-0.62; Treg 0.23-0.40.\n- SIGLEC1: CD14 0.86-1.59, CD16 0.52-0.90, Int 0.64-1.43, cDC2 0.45-0.71.\n- Carried (PASS gate and q<0.05) in 5/5 draws for 8 types and in >=3 draws for 13 types; MAIT and pDC in 0 draws. All of this is exactly as claimed.\n- Observed paired cells: MAIT +0.93 to CD16 +2.14, every p<=0.004.\n- Severity, CD14: +0.53 to +0.79, p 0.05-0.2.\n\nALTERNATIVE SCORES:\n- log2(CPM+1): CD14 +1.26 to +1.70.\n- Pseudobulk thinned to a fixed 8,000 UMI (removes differences in depth and cell number): CD14 +1.06 to +1.18; every testable type is positive in every draw.\n- Number of types carried 5/5 is 4-9 depending on the score; carried in >=3 draws is 10-13.\n\nCALIBRATION:\n- Recomputed per-draw programme gate: PASS 5/5 in 13 types.\n- Generated/observed ratio on paired tests: 0.31-1.07 with the edgeR score, 0.57-1.32 depth-equalised.\n- CD14 per-sample Spearman, generated vs observed: 0.82-0.88. Bulk score vs observed: 0.92-0.93. Partial correlation given the bulk: 0.17-0.22.\n- Calibration restricted to wave 1: PASS for the major types.\n- calibration.csv, read directly:\n  - 'ISG-high monocyte': ratio 0.65-0.81, rho 0.78-0.85.\n  - 'ISG-high T': ratio 0.29-0.60, rho 0.72-0.81.\n  - COVID and severity signs agree in 5/5 draws for both rows.\n\nCONFOUNDERS. Counts are draws with p<0.05 out of 5, for CD14 / CD16 / memCD8 / NK / gdT:\n- 5/5 in every type after each of these:\n  - dropping the sex_flag sample;\n  - dropping samples whose donor trained the draw;\n  - dropping the 3 donors that are both COVID- and COVID+;\n  - one sample per donor;\n  - the strict combination plus age and sex.\n- Wave 1 only: 5/5/5/4/5.\n- Mixed model, CD14: +1.30 to +1.66, p<=0.004.\n- Label confidence >=0.9 plus depth equalisation: CD14 +1.35 to +1.64, p<=0.005.\n- T cells with no myeloid transcripts (subsample): +0.67 to +1.01, p<=0.02. Observed: +1.38.\n- Age + sex + diabetes + ethnicity: about 20-30% smaller. CD14 (log2 CPM+1) +0.64 to +1.33, p 0.02-0.2. The bulk falls the same way, 1.38 -> 1.02. Diabetes is unbalanced: 62% of COVID+ vs 32% of COVID- donors.\n\nBEYOND BULK:\n- Bulk-only bulk ISG: +1.38 (p 1.6e-4).\n- The generated score adds to the bulk score (likelihood-ratio p<0.05) in 1 of 71 type x draw tests; CD14 p 0.15-0.98.\n- Observed cells add nothing either: COVID coefficient given the bulk score is -0.50 to +0.20.\n\nORDERING ACROSS TYPES (correlation of per-type effects, bulk-only vs observed):\n- edgeR score: r 0.74-0.86 (as claimed).\n- log2(CPM+1): r 0.20-0.66.\n- Depth-equalised log-sum: r 0.19-0.81.\n- IFI27 alone: r 0.87-0.96.\n- The other 9 ISGs (no IFI27): r 0.03-0.83.\n- Paired-test profile, generated vs observed: r 0.84-0.92.\n- Observed within-sample CD14 minus naive CD4: with IFI27, +0.51 to +0.69 (p 0.001-0.014); same 9 genes in both types, +0.25 to +0.33 (p 0.03-0.18).\n\nSEVERITY AND SAMPLING TIME:\n- Bulk-only severe samples were taken later: median day 15 vs 9.5 for mild. In the paired set it is day 9 vs 9, and 64% of paired severe samples are wave 2.\n- ISG falls by 0.09-0.17 log2 per day.\n- Adjusted for days since swab:\n  - bulk-only bulk: +1.09 (p 0.005), against +0.63 (p 0.11) raw;\n  - generated CD14: +0.92 to +1.31, p 0.001-0.008, in 5/5 draws;\n  - samples taken by day 14 only: +0.85 to +1.28;\n  - observed: +1.63.\n\nMYELOID TRANSCRIPTS IN LYMPHOID PSEUDOBULKS (COVID+ minus COVID-):\n- Observed: up to +0.99, p<0.05 in 0 of 10 types.\n- Bulk-only generated: up to +2.78, p<0.05 in 23 of 49 type x draw tests.\n- Paired-test generated: up to +3.15, p<0.05 in 29 of 50.",
+     "issues": [
+      "Severity negative check does not hold as stated. Bulk-only severe/critical samples were taken later (median day 15 vs 9.5 for mild/moderate), and ISG falls 0.09-0.17 log2 per day. After adjusting for days since positive swab, or keeping samples taken by day 14, the severity ISG difference appears in the bulk-only bulk (+1.09, p 0.005; raw +0.63, p 0.11) and in generated CD14 (+0.92 to +1.31, p<=0.008, all 5 draws), at about 0.6-0.8x of observed (+1.63). 'Does not carry over' reflects sampling time, not a cohort without the effect. SIGLEC1 in CD14 is already significant for severity in some draws without adjustment.",
+      "The ordering claim depends on how the score is built. The cross-type r of 0.74-0.86 holds only for the edgeR prior-count score. With log2(CPM+1) it is 0.20-0.66, and with a depth-equalised log-sum score it is 0.19-0.81 (about 0.2 in 3 of 5 draws). The robust part is IFI27, which rises only in myeloid cells (r 0.87-0.96 across types, generated at about 1/3-1/2 of observed). Without IFI27 the other 9 ISGs rise fairly evenly in observed cells and agreement is weak (r 0.03-0.83). 'Largest in monocytes, cDC2, NK and memory CD8' changes with the score.",
+      "Discordances in the ordering: generated Tregs respond less than the other types within the same sample (significant in 3-5 draws) while observed Tregs do not; naive CD8 relative responses are inconsistent.",
+      "Nothing beyond the bulk. The generated per-type score adds to the bulk ISG score in only 1 of 71 type x draw likelihood-ratio tests. Observed cells add nothing beyond the bulk either (COVID coefficient given bulk -0.50 to +0.20). That ISGs rise in every lineage is already reported (Stephenson 2025).",
+      "'Every major cell type' and '5/5 draws positive in all 15 types' are overstated. MAIT is testable in only 4 draws and pDC in 2 (too few COVID- donors with >=10 cells), and neither is carried in any draw. Naive B is fragile: 9-17 of 19 COVID- donors have >=10 naive B cells, it loses significance after adjusting for cell number and library size, and 5/5-carried type counts range from 4 to 9 across score definitions.",
+      "Clinical covariates: diabetes is unbalanced (62% of COVID+ vs 32% of COVID- donors) and is linked to higher ISG within COVID- donors. Adjusting for age, sex, diabetes and ethnicity shrinks effects by about 20-30% and removes significance in 1-4 draws for several types. The bulk shrinks the same way (1.38 to 1.02), so this is a property of the cohort that the generated cells copy.",
+      "Generation artefact: generated COVID+ lymphoid pseudobulks carry extra myeloid transcripts (LYZ, S100A8/9, FCN1, CST3), up to +2.8 log2 (23 of 49 tests significant). Observed cells show no significant difference (0 of 10). The lymphoid ISG coefficient shrinks when adjusted for this, but T and NK cells with no myeloid transcripts keep the ISG rise (T cells +0.67 to +1.01 vs observed +1.38).",
+      "SIGLEC1 calibration is weaker outside CD14: CD16 generated/observed ratio 0.30-0.50 and cDC2 per-sample Spearman 0.43-0.63.",
+      "Agreement across draws is not independent replication. Per-sample bulk-only ISG scores correlate at Spearman 0.75-0.88 between draws for major types, because the draws share the same bulk input and the same noise seed.",
+      "Minor numeric differences: my paired-test cross-type r is 0.84-0.92 (claimed 0.88-0.92), and the CD14 likelihood-ratio p range is 0.15-0.98 (claimed 0.22-0.93). Both are consistent with the claim in substance."
+     ],
+     "reasoning": "I wrote my own code from meta.csv, the pb_l2, markers, cells and subsample files of the bulkonly, paired_gen and observed sets, the bulk npz files and the measured-gene mask. I first checked that pb_l2 sums equal the per-cell marker counts and lib_measured. With a score built the same way as the discovery's, I reproduced its COVID+ vs COVID- numbers exactly: per-type ISG and SIGLEC1 effects, group means, q values, carrying draws (8 types in 5/5, 13 in >=3), observed effects and the severity CD14 numbers. I also tested three other score definitions (log2 CPM+1, log of the summed CPM, and binomial thinning to a fixed pseudobulk depth). The COVID effect is positive in every testable draw and type under all four.\n\nEvery contrast was analysed within each draw and gated by that draw's own recomputed calibration. I also read the calibration.csv ISG-high monocyte and ISG-high T rows; they match the claim. The COVID effect survives:\n- wave 1 only (all COVID- samples are wave 1), with wave-1 calibration recomputed;\n- removing the sex_flag sample and samples whose donor trained the draw;\n- repeated measures: donor means, a mixed model, cluster-robust OLS, one sample per donor, and removing the 3 donors that are both COVID- and COVID+;\n- age and sex;\n- equalised pseudobulk depth, which covers group differences in cell number and per-cell library;\n- label confidence >=0.9;\n- T and NK cells with no myeloid transcripts.\n\nAdjusting for diabetes and ethnicity shrinks it by about 20-30%, as it does the bulk. So the main COVID finding is reproduced and robust, and its calibration is good for the major types.\n\nThe claim is qualified, not confirmed, because:\n1. Its severity statement is confounded by sampling time. Once time since swab is controlled, the severity ISG difference does carry over to the bulk-only bulk and to the generated cells in 5/5 draws.\n2. The cross-type ordering it highlights holds only for one score definition and comes mainly from IFI27, which rises only in myeloid cells; for the rest of the programme the observed response is nearly even across lineages.\n3. The generated cells add nothing beyond the bulk ISG score.\n4. MAIT and pDC cannot be assessed, and naive B is fragile.\n5. Generated COVID+ lymphoid cells carry extra myeloid transcripts that observed cells do not. This does not remove the T and NK result.\n\nNo GPU was used. I wrote only inside my verifier directory and ran no paper-archive maintenance.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/vcommon.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/v06_misc.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/v07_severity_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/v07_wave1_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/v08_tallies.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_recompute/v08_key_numbers.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a9f07e236c3e23d3d",
+     "verdict": "qualified",
+     "recomputed": "All numbers below are per draw. Code is my own (vc_common.py and v01-v05; it does not import ip_common). With the candidate's edgeR-style normalisation it reproduces every core number exactly.\n\nCOVID+ vs COVID- in bulk-only generated cells (v01_summary.csv)\n- CD14 ISG: +1.24, 0.98, 1.14, 1.32, 0.96; p 0.0004-0.004; BH q over my 19 tests 0.0014-0.012. Draw-0 group means 7.08 vs 5.84.\n- Carrying draws (PASS gate and q<0.05): 5 draws in 8 types; at least 3 draws in 13 of 15 types; MAIT and pDC not carried.\n- Calibration ratio in the carrying draws is 0.32-1.07 (Treg 0.31-0.54).\n- CD14 per-sample Spearman, generated vs observed: 0.81-0.89. The bulk ISG score gives 0.92-0.94; the partial Spearman of generated given bulk is 0.07-0.22.\n- In all 15 types the whole-PBMC bulk score tracks observed type-level ISG better than the generated cells do. This also holds within COVID+ samples.\n- The result is robust:\n  - log2(CPM+1) normalisation: CD14 +1.26 to +1.70, ratio 1.0-1.4;\n  - without the draw's training donors: +0.90 to +1.34;\n  - wave 1 only: +0.80 to +1.10;\n  - the bulk-only donors with no paired sample at all (31 COVID+, 16 COVID- donors): +1.01 to +1.29.\n- The bulk-only generated effect is 0.18-0.73x the observed paired effect, not 0.3-1.1x. The 0.3-1.1x is the paired-test calibration ratio.\n- SIGLEC1 in CD14, CD16 and intermediate monocytes and in cDC2: calibration PASS 5/5. In cDC2 the per-sample Spearman is only 0.44-0.63, against 0.82-0.88 for bulk. In observed wave-1 samples, cDC2 is +0.51 (p 0.20) and intermediate monocytes +0.65 (p 0.16).\n- The generated CD14 ISG adds nothing to the bulk ISG10 score: logistic LR p 0.24-0.93 for COVID and 0.22-0.78 for severity (v03_bulk_lr.csv).\n\nCell-type ordering (v02, v03, v04)\n- Bulk-only generated vs observed, across types:\n  - against all paired samples, r 0.74-0.86, which reproduces the candidate;\n  - against observed wave 1, which matches the bulk-only cohort, r 0.12-0.48. The wave-1 split-half reliability is 0.55 (Spearman-Brown 0.71).\n- With log2(CPM+1), r is 0.20-0.65 against all paired and -0.41 to +0.18 against wave 1.\n- On a common sample set (at least 10 cells in all 13 types; 20 vs 18 donors), the observed 9-gene ISG effect (IFI27 left out) is uniform across types:\n  - 0.63-0.88, SD 0.075 against 0.27 on each type's own samples;\n  - CD14 is among the lowest at 0.65.\n  - The generated cells keep monocytes above T cells, for example draw 0 CD14 0.56 and naive CD4 0.03.\n  - Bulk-only vs observed r is -0.60 to +0.44 for the 9 genes and 0.19-0.62 for all 10.\n- Within-sample interaction (9 genes, reference = mean of 5 abundant types):\n  - Observed cells show no type with q<0.05; the omnibus permutation p is 0.085 (0.12 with memory CD4 as the reference).\n  - Paired-test calibration: 5-9 of 13 types are significant in generated cells against 0 of 13 in observed cells of the same samples.\n  - The generated spread is larger (SD 0.19-0.26 vs 0.12-0.14). Across types r is 0.10-0.47, and the per-sample Spearman of the interaction has a median of 0.03-0.17.\n  - Treg is -0.35 to -0.54 in generated cells and +0.08 to +0.19 in observed cells. CD14 is +0.18 to +0.27 generated and -0.11 to +0.09 observed.\n- The observed monocyte excess is driven by IFI27: +4.2 to +5.7 in monocytes against +0.1 to +2.0 in lymphocytes.\n\nSeverity, severe vs mild among COVID+ (v03, v04, v05)\n- Unadjusted, generated CD14 ISG is +0.53 to +0.79 (p 0.05-0.16), as the candidate reports.\n- But bulk-only severe samples were taken later: median 15 days after the positive swab, 45% within 14 days. Bulk-only mild samples: 9.5 days, 90%. Paired severe samples: 9 days, 80%.\n- Bulk ISG falls with time: Spearman -0.37 (bulk-only) and -0.48 (paired).\n- Within 14 days, generated CD14 ISG is +0.85 to +1.28 (p 0.0045-0.06; 4 of 5 draws p<0.05).\n- Adjusted for time, it is +0.92 to +1.31 (p 0.0013-0.0075, 5 of 5 draws).\n- Using each donor's earliest sample within 14 days, the time-adjusted effect is +0.91 to +1.36 (p<0.04, 5 of 5).\n- Generated SIGLEC1 in CD14 monocytes shows the same: +1.03 to +1.69 (p 0.002-0.045) using each donor's earliest sample within 14 days.\n- The bulk-only bulk ISG10 rises from +0.63 (p 0.11) unadjusted to +1.08 (p 0.010) within 14 days and +1.09 (p 0.005) time-adjusted.\n- Severity calibration within 14 days: generated +1.24 to +1.87 against observed +1.87 to +2.03, which passes.",
+     "issues": [
+      "Sub-claim refuted: 'the cross-type ordering follows the observed one'. The candidate's r 0.74-0.86 is mostly a sample-selection effect. Lymphopenic COVID+ samples, which carry high ISG, drop out of the naive T, Treg, MAIT and memory B estimates in both the observed and the generated cells. On a common sample set the observed 9-gene response is flat across types (0.63-0.88; CD14 among the lowest at 0.65). Bulk-only vs observed then gives r between -0.60 and +0.44 for the 9 genes.",
+      "The within-sample ordering fails paired calibration. In the same test samples, generated cells show 5-9 of 13 types significantly different from the reference, while observed cells show none. Generated Treg sits below the reference (-0.35 to -0.54) while observed Treg sits slightly above it (+0.08 to +0.19). Generated CD14 is over-localised. The per-sample Spearman of the interaction has a median of 0.03-0.17. 'Largest in monocytes, cDC2, NK and memory CD8' is therefore a generator pattern, not calibrated biology. The observed monocyte excess comes from IFI27, which the generator reproduces at about half strength.",
+      "Against observed wave 1, which makes up most of the bulk-only cohort, the profile correlation is only 0.12-0.48. With log2(CPM+1) normalisation it is -0.41 to +0.18. The ordering depends on the choice of comparison wave, normalisation and sample set.",
+      "Sub-claim refuted: 'the larger severe-vs-mild ISG difference does not carry over to the bulk-only cohort'. The gap is a sampling-time confound: bulk-only severe samples have a median of 15 days against 9 days in paired severe samples, and ISG decays with time. Within 14 days, or with time adjustment, generated CD14 ISG shows +0.9 to +1.3 log2 in 5 of 5 draws (p 0.0013-0.0075). Memory CD8 ISG and CD14 SIGLEC1 show the same, and so does the bulk itself (+1.09, p 0.005). This matches Stephenson 2025 PDF p.1 and p.9 and Gisby 2022 PDF p.8, which report a larger IFN response early in severe disease. It is still not beyond-bulk.",
+      "Overstatements and inaccuracies:\n- '5/5 draws positive in all 15 types' is wrong: MAIT has data in only 4 draws and pDC in only 2.\n- 'Every major cell type': MAIT and pDC are not carried.\n- 'The size is 0.3-1.1x of the observed paired effect' is the paired calibration ratio. The bulk-only effect relative to the observed paired effect is 0.18-0.73x.",
+      "'The generated score adds nothing (LR p 0.22-0.93 in all contrasts)' is true for ISG only. Generated CD14 SIGLEC1 adds to a bulk ISG10 baseline for COVID in draw 4 (p 0.039) and for severity in draws 3 and 4 (p 0.0024 and 0.033). This happens in 2 of 5 draws, among about 60 tests, so it is not robust.",
+      "Mis-citation: Gisby 2022 PDF p.2 reports SIGLEC1 (with SIGLEC9 and SELL) rising in the PBMC bulk, not 'IFN pathways in the bulk'. The IFN-alpha/beta pathway enrichment (DDX58/IFIH1, ISG15, STAT1) is on PDF p.5 and Fig. 2c (legend PDF p.4), and it describes the plasma proteome. The transcriptome type I IFN GSVA score is mentioned only on PDF p.7. Gisby PDF p.7 also states that SIGLEC-1 protein is 'exclusively expressed by CD14+ monocytes'. Generated SIGLEC1 in cDC2 is therefore weak: cDC2 Spearman is 0.44-0.63, and observed wave 1 shows +0.51 (p 0.20).",
+      "Novelty: none for this cohort. Stephenson 2025 PDF p.4 reports, in the 2020 (wave-1) cohort, an enrichment of IFN-alpha and -beta response pathways across a broad range of innate and adaptive immune cells. Gisby's bulk analysis (PDF p.2) already includes the bulk-only samples: 179 COVID+ samples from 51 patients and 55 COVID- samples in wave 1. For contrast, non-ESKD cohorts report a lack of type I IFN or ISG states in severe disease: Arunachalam 2020 PDF p.1 and Combes 2021 preprint PDF p.2 and p.9.",
+      "The five draws share the noise seed, so '5/5 draws' is not five independent replications. The per-draw calibration gates are the stronger evidence."
+     ],
+     "reasoning": "(1) Calibration. The ISG programme and SIGLEC1 are not rows in calibration.csv, so I recomputed the paired check for each draw with independent code; it matches the candidate exactly. For COVID+ vs COVID- the gate passes in all 5 draws for 12 of 15 types, with ratios of 0.3-1.07 and the correct sign. The calibration.csv rows for ISG-high monocytes and ISG-high T cells agree (ratio 0.65-0.81 and 0.29-0.60; sign 5/5). There is no shift between paired and bulk-only bulk ISG within the COVID- or COVID+ mild groups, so the calibration transfers. The core rise in ISG and in SIGLEC1 with COVID therefore holds in the bulk-only cohort:\n- in 13 of 15 types with at least 3 carrying draws;\n- in donors with no single cells at all;\n- without training donors, in wave 1 only, and under both normalisations.\n\n(2) Observed paired cells show the same rise in every type (p<=0.004). The claimed beyond-bulk content, that the cell-type ordering follows the observed one, fails:\n- the agreement comes from shared sample selection and IFI27;\n- on common samples and within samples, observed cells show no ordering;\n- the generator invents one, with Treg in the opposite direction.\n\n(3) Biology and novelty. The finding is already reported in Stephenson 2025 (PDF p.4) and Gisby 2022 (PDF p.2 and p.7-8). One of the candidate's citations is wrong (Gisby p.2 'IFN pathways in the bulk'). The severity-ISG link in this cohort is time-dependent: Stephenson PDF p.1 and p.9, Gisby PDF p.8. Correcting for sampling time shows that the severity difference does appear in the bulk-only generated cells, which contradicts the claim's negative statement.\n\n(4) Beyond bulk: nothing. The bulk ISG score tracks observed type-level ISG better than the generated cells in every type. The generated CD14 ISG adds nothing to the bulk ISG10 score (LR p>=0.22). The only per-type information, the ordering, is uncalibrated.\n\nVerdict: qualified. The headline association is calibrated, observed, already published and fully explained by the bulk. Two sub-claims are refuted: the ordering and the severity non-transfer.\n\nWhich draws carry what:\n- COVID+ vs COVID- ISG: all 5 draws for CD14, CD16 and intermediate monocytes, CD56dim NK, memory CD4 and CD8, cDC2 and gdT.\n- SIGLEC1: all 5 draws in all three monocyte types; draws 0, 1, 3 and 4 in cDC2.\n- Time-adjusted severity: all 5 draws.\n\nCPU only, under memory caps; no GPU. I wrote only to my verify dir. No archive delta from this verifier; registering these tables, if they become paper evidence, is for the coordinating agent.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/v01_core.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/v04_reliability_wave1.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/v04_severity_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/v05_severity_robust.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/v05_severity_robust.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/pdftext/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/pdftext/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/pdftext/Combes2021_Absence_protective_ISG_states_severe_COVID19_preprint.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-4_calib/pdftext/Arunachalam2020_Science_systems_immunity_mild_vs_severe_COVID19.txt"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-LONG-1",
+   "lens": "imp_longitudinal",
+   "final": "qualified",
+   "verdict_recompute": "confirmed",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 2,
+    "theme": "interferon (positive control)",
+    "synthesis_strength": "strong positive control for ISG-high monocytes (one verifier confirmed); ISG-high T only in draws 0-2; nothing beyond bulk",
+    "reportable_statement": "In bulk-only COVID+ samples the generated ISG-high share of monocytes falls with days since the first positive swab in all five draws (-0.238, -0.186, -0.219, -0.160, -0.172 logit/day; p 8e-13 to 5e-17), also in the 31 COVID+ donors without single cells (-0.19 to -0.28) and within donors (-0.17 to -0.25). The fall is front-loaded and follows the bulk ISG score, which explains it completely. ISG-high T cells behave the same way in draws 0-2.",
+    "carrying_draws": {
+     "ISG-high monocyte, time": "0-4 (slope ratio 0.68-1.00; draws 3-4 decline ~30% less steeply than observed on the same samples)",
+     "ISG-high T, time": "0,1,2 (0,1,2,4 in wave 1)"
+    },
+    "honesty": {
+     "calibration": "calibration.csv ISG-high monocyte: ratio 0.65-0.81, Spearman 0.78-0.85, COVID and severity signs 5/5. In synthesis_imperial_eskd/i01_codes_by_feature.csv it passes 5/5 for COVID (all and wave-1 tests) and for severity among COVID+ (all and wave-1); with the official severity row (recovery samples in the severe arm) the observed contrast is not significant in draws 1 and 3. Time contrast recomputed on COVID+ paired tests: ratio 0.65-0.83, Spearman 0.84-0.89, within-donor r 0.88-0.94; generated slope -0.193 to -0.272 vs observed -0.251 to -0.301 (ratio 0.68-0.99); wave-1 slope ratio 0.72-1.00. Calibration transfers: the ISG bulk genes do not differ between paired and bulk-only samples within any window (smallest BH q 0.97).",
+     "replication": "5/5 draws. Survives age/sex/ethnicity, sex flag, training donors, onset-based time, WHO at sample, fatal outcome, generated depth (cells thinned to 800 counts; library-quartile standardisation) and label confidence. Per-sample residuals after removing bulk ISG still correlate across draws (r 0.31-0.69): shared noise.",
+     "observed": "Observed paired 0.70 -> 0.47 -> 0.19 by week (COVID- 0.14); slope -0.282 to -0.286 (p 4.5e-28); wave 1 -0.222, wave 2 -0.340 (interaction p 0.018); within donors -0.29. Bulk-only wave-1 slopes are 0.76-1.20x the observed wave-1 slope.",
+     "beyond_bulk": "None for the trend. Adjusting for the 10-gene bulk ISG score removes it in observed (-0.003 to -0.011, p >= 0.58) and generated cells (-0.022 to +0.008); the bulk score tracks the observed fraction better (Spearman 0.90-0.92 vs 0.84-0.89)."
+    },
+    "verdict_reasons": [
+     "Recompute verifier: confirmed. The monocyte claim reproduces exactly and survives every confounder.",
+     "Calib verifier: qualified. ISG-high T is calibrated only in draws 0-2 (level 0.29 in draw 3, 0.44 in draw 4).",
+     "'Falls steadily over 3 weeks' is wrong: the decline is front-loaded, following the convex bulk ISG curve.",
+     "The >21-day window is uncalibrated: paired data stop at day 20 and all 16 later bulk-only samples are severe/critical.",
+     "Bulk-only visits of donors who also have paired visits fall about half as fast (-0.09 to -0.16), because their bulk ISG falls half as fast.",
+     "The observed severity x time steepening (-0.142, p 0.006; Stephenson Fig. 4B) is absent in bulk-only cells and in their bulk: severe bulk-only donors are first sampled later (median day 9 vs 4).",
+     "Minor misquotes: p range, no-single-cell slope range, observed n 112."
+    ],
+    "refuted_subclaims": [
+     "falls steadily over 3 weeks",
+     "same result for ISG-high T in every draw"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 4A-B (PDF p.8-9) and PDF p.14; Gisby 2022 PDF p.8 (plasma IFN proteins high early then falling; SIGLEC1 higher in severe disease at days 0-3).",
+    "next_steps": [
+     "Keep as the cohort positive control: monocytes in all draws, T cells only in draws 0-2.",
+     "Restrict trajectory claims to <=20 days, or add late paired samples.",
+     "Add time and severity x time contrasts to the trust layer."
+    ],
+    "key_sources": [
+     "verify/IMP-LONG-1_recompute/v10_numbers.txt",
+     "verify/IMP-LONG-1_recompute/v03_bulkonly_models.csv",
+     "verify/IMP-LONG-1_recompute/v04_calibration.csv",
+     "verify/IMP-LONG-1_recompute/v05_beyond_bulk.csv",
+     "verify/IMP-LONG-1_recompute/v07_shape_matched.csv",
+     "verify/IMP-LONG-1_calib/v01_temporal_calibration.csv",
+     "verify/IMP-LONG-1_calib/v03_observed.csv",
+     "verify/IMP-LONG-1_calib/v04_b_adjusted.csv",
+     "verify/IMP-LONG-1_calib/v04_d_transfer_genes.csv",
+     "verify/IMP-LONG-1_calib/v08_sevxday_bulkscore.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-LONG-1",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only COVID+ donors, the generated ISG-high share of monocytes (and of T cells) is highest at days 0-7 after the first positive swab and falls steadily over 3 weeks: -0.16 to -0.25 logit/day in every draw, including the 31 donors who have no single cells. This matches the decline in observed paired cells from the same wave. The bulk ISG score explains this trajectory completely, so this is a positive control.",
+    "kind": "trajectory",
+    "feature": "state | ISG-high monocyte (positive control); same result for state | ISG-high T",
+    "comparison": "Days since first positive swab (0-21 d), with windows d0-7 / d8-14 / d15-21 / >21. COVID+ samples only. Bulk-only generated cells per draw vs observed paired cells, also wave-matched (bulk-only is 89% wave 1).",
+    "effect_bulkonly": "Raw window means (ISG-high share of monocytes), d0-7/d8-14/d15-21/>21:\n- d0: 0.47/0.21/0.12/0.09\n- d1: 0.47/0.20/0.10/0.10\n- d2: 0.46/0.24/0.13/0.13\n- d3: 0.48/0.27/0.19/0.18\n- d4: 0.43/0.21/0.14/0.11\n\nSeverity-adjusted slopes (logit/day, <=21 d):\n- All bulk-only (127 samples / 57 donors): -0.238, -0.186, -0.219, -0.160, -0.172.\n- Wave 1 only (110 / 47): -0.246, -0.181, -0.217, -0.168, -0.181.\n- No-single-cell donors (77 / 31, all wave 1): -0.283, -0.215, -0.262, -0.194, -0.215.\n\nWindow effects vs d0-7: d15-21 is -1.9 to -2.9 logit; >21 d is -2.4 to -3.7.\nISG-high T, wave-1 slopes: -0.157 to -0.239.",
+    "stats": "Model: statsmodels MixedLM (ML), logit(fraction) ~ days + severity_peak + wave + (1|donor).\n- Per-draw p: 2e-13 to 5e-17.\n- Wave-1 BH q (51 features within each draw): 1e-9 to 8e-15.\n- Within-donor fixed-effect slope: -0.166 to -0.254 (p <= 3e-5).\n- Unchanged with onset-based time (Stephenson's definition) and with age/sex/ethnicity adjustment.\n- Sensitivity without training donors and without the sex-flag sample: -0.172 to -0.254.",
+    "draws_same_sign": "5/5. All draws used seed 20260927 and the same bulk input, so these are not independent replications.",
+    "paired_calibration": "calibration.csv: ratio 0.65-0.81, Spearman 0.78-0.85; COVID+ vs COVID- and severity signs agree in 5/5 draws.\n\nRecomputed on COVID+ paired test samples (s03):\n- Ratio 0.65-0.83, Spearman 0.84-0.89, within-donor tracking r 0.88-0.94.\n- Slope: generated -0.195 to -0.276 vs observed -0.254 to -0.303. TIME_PASS 5/5.\n\nWave-1 restriction (s16): slope ratio 0.72-1.00, W1_TIME_PASS 5/5.\nISG-high T: TIME_PASS in draws 0,1,2; W1 in draws 0,1,2,4.",
+    "paired_observed_concordance": "Yes.\n- Observed paired COVID+ cells (113 samples / 35 donors, native depth): 0.70 -> 0.47 -> 0.19 (COVID- 0.14). Slope -0.285 logit/day (q 3e-26).\n- By wave: -0.222 in wave 1, -0.340 in wave 2 (wave x day p 0.018).\n- Bulk-only wave-1 slopes are 0.76-1.11x the observed wave-1 slope.\n- Peak window is d0-7 in every set. Within-donor FE slope in observed cells: -0.291.",
+    "beyond_bulk": "Nothing beyond the bulk.\n- The bulk ISG score (the 10 rule genes) tracks the observed fraction better than the generated cells do: Spearman 0.915 vs 0.875, within-donor r 0.939 vs 0.912 (medians over draws).\n- The bulk ISG score itself falls (-0.095 SD/day in bulk-only, p 3e-18).\n- Adjusting for it removes the time effect in the observed truth (-0.003, p 0.88) and in every draw (-0.014 to +0.003, p >= 0.38).",
+    "novelty": "Already reported; this is a positive control.\n- Stephenson 2025 Cell Genomics, PDF p.9 (journal p.8), Fig. 4A: IFN-stimulated cell subtypes rise in week 1 and then gradually fall.\n- Gisby 2022 Nat Commun, PDF p.8: SIGLEC1 / type I IFN is highest at days 0-3 (more so in severe disease).",
+    "strength": "strong",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s02_observed_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s03_temporal_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s04_bulkonly_traj.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_tracking.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_adjusted.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s09_shapes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s13_within_donor.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s15_wave_matched.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_bulkonly.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s17_wave1_beyond_bulk.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s21_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a455ee2447034bf0c",
+     "verdict": "confirmed",
+     "recomputed": "All numbers come from my own code (v01–v10) run on the raw cells.parquet and markers.npz files (CPU only, 6G scope). The quoted values are in v10_numbers.txt.\n\nRECOMPUTE. My ISG-high monocyte and ISG-high T parent and state counts match states.parquet exactly in all 22 bulkonly / paired_gen / observed blocks. My re-thinned observed values agree with observed_thin_d<j> (r >= 0.997).\n\nBULK-ONLY, ISG-HIGH MONOCYTE (COVID+, <=21 d, 127 samples / 57 donors)\n- LMM_ML (lfrac ~ days + sev + w2, donor random intercept), slope per draw: -0.238, -0.186, -0.219, -0.160, -0.172; p 1e-15, 8e-13, 2e-14, 2e-13, 5e-17. 5/5 negative.\n- Other estimators, all negative in 5/5 draws:\n  - REML: identical.\n  - OLS, donor-clustered: -0.158 to -0.218.\n  - Binomial GLM, clustered sandwich: -0.148 to -0.215.\n  - Within-donor FE (108 samples / 38 donors): -0.166 to -0.254, p <= 3e-5.\n  - Per-donor slopes: 26-28 of 35 donors negative, Wilcoxon p <= 5e-4.\n  - One sample per donor (500 reps): rho -0.29 to -0.34.\n- Raw window means, d0-7 / d8-14 / d15-21 / >21: draw 0 .471/.211/.124/.091 … draw 4 .431/.209/.144/.107.\n- Subsets:\n  - Wave 1 (110/47): -0.168 to -0.246.\n  - No-sc donors (77/31, all wave 1): -0.194 to -0.283; FE -0.201 to -0.309.\n\nCONFOUNDER CONTROLS (monocyte; all 5/5 negative, p <= 1e-9 unless stated)\n- +age+sex+ethnicity: -0.150 to -0.230.\n- Without the sex_flag sample and training donors: -0.172 to -0.254.\n- Onset-based time: -0.182 to -0.276.\n- +WHO severity at sample: -0.154 to -0.240.\n- +fatal: -0.160 to -0.241. Non-fatal donors only: -0.152 to -0.235.\n- +bulk library size: -0.159 to -0.237. +bulk monocyte score: unchanged.\n- +log parent cell number: -0.151 to -0.224.\n- +generated depth: -0.142 to -0.238.\n- Fixed per-cell depth (cells thinned to 800 counts): -0.195 to -0.266.\n- Library-quartile standardised (no cell selection): -0.180 to -0.247.\n- L2 confidence >= 0.9: -0.164 to -0.237.\n- All-in, OLS clustered: -0.105 to -0.230, p <= 9e-4.\n\nBULK ISG ADJUSTMENT\n- +isg10: -0.022 to -0.001, p >= 0.1.\n- +isg10, FE: p >= 0.3.\n- +20-gene leave-rule-out ISG score: -0.028 to -0.004, p >= 0.05.\n- The bulk isg10 score itself falls -0.093 SD/day (p 3e-19).\n\nISG-HIGH T, bulk-only\n- LMM slopes: -0.241, -0.202, -0.218, -0.154, -0.188.\n- Fixed depth: -0.196 to -0.250. Quartile-standardised: -0.175 to -0.229.\n- +generated depth: draws 3 and 4 shrink to -0.071 and -0.112. Generated T library tracks the IFN state, r(log lib, isg10) -0.65, so this is over-adjustment.\n\nOBSERVED TRUTH (paired COVID+, native depth, 112 samples / 35 donors for monocytes)\n- Windows: 0.703 / 0.465 / 0.188; COVID- 0.140.\n- Slope -0.286; adjusted -0.282.\n- Wave 1 -0.222; wave 2 -0.340; wave x day p 0.018. FE -0.291.\n- Wave 1 at each draw's matched depth: -0.201 to -0.222. Bulk-only wave-1 slope ratio vs this: 0.76-1.20.\n- Adjusted for isg10: -0.010 (p 0.62).\n\nCALIBRATION\n- calibration.csv rows:\n  - Monocyte: ratio 0.645-0.809, Spearman 0.782-0.852, COVID+/- and severity signs 5/5.\n  - T: ratio 0.585, 0.517, 0.598, 0.285, 0.435; Spearman 0.72-0.81; signs 5/5.\n- COVID+ test samples (102-104 per draw), monocyte:\n  - Ratio 0.65-0.82, Spearman 0.83-0.89, within-donor r 0.89-0.94.\n  - Generated slope -0.193 to -0.272 vs observed -0.249 to -0.301; slope ratio 0.68-1.00.\n  - Wave-1 slope ratio 0.74-0.99.\n  - Gate passed in draws 0-4.\n- COVID+ test samples, T: ratio 0.61, 0.54, 0.63, 0.30, 0.45. Gate passed in draws 0, 1, 2 only.\n\nTRANSFER\n- Paired vs bulk-only isg10 within each window: Mann-Whitney p 0.13-0.82.\n- Bulk-to-generated mapping slope, paired vs bulk-only: interaction p >= 0.2.\n\nBEYOND BULK\n- Spearman, bulk score vs observed 0.91-0.92; generated vs observed 0.84-0.88.\n- Within-donor r: bulk 0.93-0.94; generated 0.89-0.94.\n- Leave-one-donor-out MSE shows no consistent gain from adding the generated fraction.\n\nSHAPE\n- Bulk-only 3-4-day bins: d0-3 0.62-0.69, d4-7 0.31-0.34, d8-10 0.30-0.36, d11-14 0.16-0.22, d15-21 0.10-0.19.\n- days^2 term > 0 in all draws (p 0.002-0.07).\n- Observed paired cells: 0.77 / 0.67 / 0.55 / 0.42 / 0.19, no curvature (p 0.25).\n\nNON-INDEPENDENCE OF DRAWS. After removing isg10, per-sample residuals still correlate across draws: r 0.31-0.69, mean 0.54.\n\nNEGATIVE CONTROL (platelet signal in monocytes): bulk-only -0.007 to -0.057/day; observed -0.025.",
+     "issues": [
+      "Wording: 'falls steadily over 3 weeks' is wrong for the bulk-only curve. The fall is front-loaded: d0-3 0.62-0.69 halves to 0.31-0.34 by d4-7, stays flat to d8-10 (0.30-0.36), then falls more slowly. The days^2 term is positive in all 5 draws (p 0.002-0.07). This is not a generator artefact. On the same paired test samples, generated and observed curves have the same shape (curvature ~0 in both). The bulk-only shape follows the bulk-only samples' own bulk ISG score, which is itself convex (p 0.005), and the curvature disappears after adjusting for that score. Suggested wording: 'highest in days 0-7 and falling mostly within the first week, as the bulk ISG score does'.",
+      "ISG-high T extension: the bulk-only decline holds in 5/5 draws (it also survives fixed-depth and library-quartile standardisation), but the calibration gate passes only in draws 0-2. In draws 3 and 4 the level ratio is 0.29/0.44 in calibration.csv and 0.30/0.45 on COVID+ test samples. Report the T-cell result for draws 0-2 only, not 'in every draw'.",
+      "ISG-high T, draws 3-4: adding generated T-cell library depth as a covariate cuts the slopes to -0.071 and -0.112 (all-in OLS draw 3: -0.055, p 0.05). This is over-adjustment, not a depth artefact. Generated T depth is lower in high-IFN samples (r with bulk ISG -0.65), which works against the detection rule; fixed-depth slopes are -0.196 and -0.219. Those draws are uncalibrated for T anyway.",
+      "Numeric misquotes, minor: (a) the headline range '-0.16 to -0.25 ... including the 31 donors who have no single cells' does not fit that subset, whose slopes are -0.194 to -0.283. (b) The per-draw p range is 8e-13 (draw 1) to 5e-17, not '2e-13 to 5e-17'. (c) The observed monocyte truth has 112 samples / 35 donors (113 for T).",
+      "Generated levels are below observed at matched depth: monocyte 0.65-0.82x, T 0.30-0.63x. Bulk-only window means (d0-7 0.43-0.48) are model fractions, not true fractions. Do not set them against the native-depth observed 0.70. The quoted 0.76-1.11x wave-1 slope ratio uses native-depth observed; at each draw's matched depth it is 0.76-1.20x.",
+      "The '5/5 draws' agreement is not independent replication. All draws share seed 20260927 and the same bulk input, and after removing the bulk ISG component the per-sample residuals still correlate across draws (r 0.31-0.69, mean 0.54). The evidence rests on per-draw calibration and within-donor tests, not on the agreement count.",
+      "Beyond-bulk nuance: for the time trend the generated cells add nothing beyond the bulk, confirmed with the 10 rule genes and with a 20-gene leave-rule-out ISG score (draw 4 residual -0.028, p 0.05). For the level, the generated fraction adds a small in-sample increment over the bulk score in 4/5 draws (b 0.24-0.47, p <= 1e-3), but leave-one-donor-out prediction does not improve consistently (MSE change -0.03 to +0.04).",
+      "Specificity: the negative-control state (platelet signal in monocytes) also drifts down slightly in bulk-only draws 0 and 3 (-0.057/day, p 9e-4; observed truth -0.025, p 0.09). The ISG decline is 3-4x larger, so it is specific, but there is a small generic downward drift in some draws."
+     ],
+     "reasoning": "Verdict: confirmed. I could not refute the claim. The monocyte decline reproduces exactly, survives every confounder I tried, and is well calibrated in all five draws. It should go forward with three corrections: T cells only in draws 0-2, a \"front-loaded, bulk-driven\" description of the curve shape, and the small number fixes listed under issues. It stays a positive control with nothing beyond the bulk.\n\nWhat I did:\n1. Read DATA_GUIDE.md and the discovery agent's scripts, only to locate inputs and definitions.\n2. Re-implemented the state rule from the raw cells and markers files: parent = AIFI L1/L2 label; ISG-high = at least 4 of the 10 ISG genes with count > 0. My counts match states.parquet exactly for the bulkonly, paired_gen and observed sets. My own re-thinning of the observed cells reproduces observed_thin_d<j>.\n3. Computed bulk scores from the actual generation conditions: the 10 rule genes, plus a 20-gene leave-rule-out ISG score, monocyte and T-cell gene scores, and raw bulk library size.\n\nRecomputing the effect:\n- The bulk-only slopes and window means match the claim to three decimals in every draw: all bulk-only samples, wave 1, no-single-cell donors, and the within-donor fixed-effect slope.\n- Every estimator I used gives a negative slope in all five draws: ML and REML mixed models, OLS with donor-clustered errors, a binomial GLM with clustered sandwich errors, within-donor fixed effects, per-donor slopes, and one random sample per donor. I avoided the statsmodels X2 dispersion bug.\n\nConfounders I tested, by model (none removes the effect):\n- Age, sex and ethnicity; wave (wave 1 only).\n- Dropping the sex_flag sample and each draw's training-donor samples.\n- Repeated measures; onset-based time; WHO severity at sample.\n- Fatal outcome, both as a covariate and as non-fatal donors only, which covers informative dropout.\n- Bulk library size and bulk monocyte content.\n- Generated parent cell number.\n- Generated depth, three ways: as a covariate, by thinning every cell to 800 counts, and by standardising over library quartiles without dropping cells.\n- Label confidence (L2 confidence >= 0.9).\n\nThe generated monocyte depth rises with time in draws 3-4. That works against a detection artefact, so depth-controlled slopes are, if anything, steeper.\n\nCalibration:\n- The calibration.csv rows are strong for the monocyte state, with both contrast signs agreeing in 5/5 draws.\n- Restricting to COVID+ test samples and to wave 1 also passes for monocytes in 5/5 draws, with generated time slopes 0.68-1.00x the observed slopes.\n- The calibration transfers to bulk-only samples. Paired and bulk-only bulk ISG scores have the same distribution within each window, and the bulk-to-generated mapping has the same slope and intercept in both sets.\n- The observed paired truth reproduces: steady decline, wave-1 slope -0.222, and the time effect vanishes after adjusting for the bulk ISG score.\n\nOn honesty point 4 (beyond bulk), the claim is right: the bulk ISG score explains the trajectory completely, in both the generated and the observed cells.\n\nWhere the claim is weaker:\n- The ISG-high T part holds in all draws as a bulk-only effect, but the guide's ratio gate fails in draws 3-4, so it is reportable only in draws 0-2. The claim already discloses this in its calibration field, but its headline says \"in every draw\".\n- \"Falls steadily\" mis-describes the bulk-only curve. It is front-loaded because the bulk-only samples' bulk ISG score is front-loaded. On matched samples the generator reproduces the observed shape.\n- A few quoted numbers are slightly off: the no-single-cell donor range, one p-value, and an n.\n\nWhy confirmed rather than qualified: the rubric's own test is \"reproduces and survives\", which the primary (monocyte) claim meets with strong calibration in every draw. Nothing I tried reverses it or makes it disappear.\n\nI wrote only under my own directory and started no GPU work. This verifier made no paper-archive delta; the coordinator owns the policy update.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v10_numbers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v10_numbers.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v01_states_long.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v07_bins_matched.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v08_negative_control.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v08_negative_control.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v09_depth_standardised.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v09_depth_standardised.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/v09_retention.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_recompute/run.sh"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "abd6fa3739c4ea39f",
+     "verdict": "qualified",
+     "recomputed": "All numbers below are my own, computed directly from features/imperial_eskd (meta, states, calibration) and the raw bulk. I did not reuse the lens tables. Models are ML MixedLM on the empirical logit with a donor random intercept, COVID+ samples only, days <= 21, adjusted for severity_peak and wave unless stated.\n\nCALIBRATION, calibration.csv (all test samples of each draw)\n- ISG-high monocyte: ratio 0.680/0.751/0.809/0.657/0.645 (draws 0-4); Spearman 0.78-0.85. COVID+ vs COVID- and severity signs agree in 5/5 draws.\n- ISG-high T: ratio 0.588/0.517/0.598/0.286/0.435; Spearman 0.72-0.81; signs agree 5/5.\n- No test sample of any draw belongs to a donor that trained that draw (overlap 0).\n\nTIME CALIBRATION, recomputed on COVID+ paired test samples (101-104 samples / 32 donors per draw; paired_gen vs observed_thin_d<j>)\n- Monocyte:\n  - ratio 0.71/0.77/0.83/0.67/0.65; Spearman 0.84-0.89; within-donor tracking r 0.88-0.94.\n  - Slope, generated: -0.272/-0.257/-0.237/-0.203/-0.193. Observed: -0.276/-0.259/-0.251/-0.301/-0.274. Ratio 0.99/0.99/0.95/0.68/0.70.\n  - Paired slope-bias test: not significant in draws 0-2 (p 0.50-0.92). Draws 3 and 4 decline less steeply (+0.096 and +0.082 logit/day, p<0.001).\n  - Time gate passes in draws 0,1,2,3,4. Wave 1 also passes in all 5 (slope ratio 0.72-1.00).\n- T: time gate passes only in draws 0,1,2 (ratio 0.30 in draw 3 and 0.45 in draw 4 fail the level gate). In wave 1 it passes in draws 0,1,2,4.\n\nBULK-ONLY, per draw (127 samples / 57 donors)\n- Monocyte slope: -0.238 (p 1.4e-15), -0.186 (8.0e-13), -0.219 (1.6e-14), -0.160 (2.2e-13), -0.172 (4.8e-17).\n- Wave 1 (110/47): -0.246, -0.181, -0.217, -0.168, -0.181. BH over 50 features gives q 2.3e-12 to 7.7e-15, except draw 1 (1.0e-9).\n- Donors with no single cells (77/31, all wave 1): -0.283, -0.215, -0.262, -0.194, -0.215.\n- Without training donors and the sex-flag sample: -0.254, -0.192, -0.217, -0.172, -0.180.\n- Robustness:\n  - Within-donor (fixed-effect) slope -0.166 to -0.254 (p<=2e-7).\n  - 74-80% of the 35 per-donor slopes are negative (Wilcoxon p 1e-6 to 5e-4).\n  - Donor-bootstrap 95% CIs exclude 0.\n- Window effects vs d0-7: d15-21 -1.93 to -2.88; >21 d -2.39 to -3.69.\n- Consecutive windows:\n  - d0-7 to d8-14 and d8-14 to d15-21 both drop significantly in 5/5 draws (p<=0.002).\n  - d15-21 to >21 d is not significant (p 0.16-0.30).\n- T: slopes -0.241/-0.202/-0.218/-0.154/-0.188; wave 1 -0.239/-0.194/-0.217/-0.157/-0.197.\n\nOBSERVED PAIRED CELLS (native depth, 112 samples / 35 donors)\n- Window means 0.703 -> 0.465 -> 0.188; COVID- 0.140.\n- Slope -0.282 (p 4.5e-28); within-donor -0.291; 84% of donor slopes negative.\n- By wave: wave 1 -0.222, wave 2 -0.340 (wave x day p 0.018). Severity x day -0.142 (p 0.006).\n- Depth-matched observed: slope -0.264 to -0.282; wave 1 -0.206 to -0.222.\n- Bulk-only wave-1 slopes are 0.76-1.11x the native observed wave-1 slope and 0.76-1.15x the depth-matched one.\n\nBEYOND THE BULK (bulk ISG score = mean log2 CPM of the 10 rule genes)\n- On test samples, the bulk score tracks the observed fraction better than the generated cells do:\n  - Spearman 0.90-0.92 (score) vs 0.84-0.89 (generated);\n  - within-donor r 0.93-0.94 vs 0.88-0.94.\n- The time slope disappears after adjusting for the bulk score: observed -0.011 (p 0.58); bulk-only -0.021 to -0.001 (p>=0.13).\n- The bulk score itself falls by 0.098 SD/day in bulk-only samples (p 9e-20).\n- The generated fraction adds some cross-sectional information about the observed level beyond the score (draws 0,2,3,4: p 6e-7 to 2e-4), but nothing about the time trend.\n\nTRANSFER (do paired-test results apply to bulk-only samples?)\n- The generator's map from bulk score to generated logit is the same in paired-test and bulk-only samples (offset p 0.13-0.74; slope difference p 0.07-0.68).\n- The 10 ISG genes do not differ between bulk-only and paired bulk within any window (smallest BH q 0.97).\n\nDEPTH (is this a library-size artefact?) — no\n- With every monocyte thinned to 600 UMI, the bulk-only slope is -0.19 to -0.27.\n- Monocyte pseudobulk ISG CPM falls by 0.12-0.16 log2 per day.\n- The generated parent library is flat with time, or rises in draws 3-4, which works against the decline.\n\nOTHER ROBUSTNESS\n- Onset-based time (Stephenson's definition): slope -0.163 to -0.256.\n- Age/sex/ethnicity adjusted: -0.150 to -0.230.\n\nSUBSET DIFFERENCE AND SEVERITY\n- Bulk-only visits of the 26 donors who also have single cells (50 samples) fall about half as fast: -0.093 to -0.162. The no-sc vs sc difference is p<0.03 in draws 0,2,3,4 and p 0.12 in draw 1.\n- This difference comes from the bulk: in those visits the bulk score falls by 0.055 SD/day, against 0.119 in the no-sc donors.\n- Severity x day in bulk-only generated cells is about 0 (p>=0.68). Paired-test generated cells reproduce the observed negative interaction in 5/5 draws, and the bulk-only bulk score also lacks it (p 0.72). Cause: severe bulk-only donors are first sampled later (median day 9 vs day 4).",
+     "issues": [
+      "ISG-high T is only partly calibrated. It passes the level and time gates in draws 0, 1 and 2 only. Draw 3 fails (ratio 0.29-0.30) and draw 4 fails on all COVID+ test samples (0.435-0.449); in wave 1 draws 0, 1, 2 and 4 pass. The headline 'same result for ISG-high T' / 'in every draw' and the quoted T range -0.157 to -0.239 include the uncalibrated draw 3. Restrict the T result to draws 0-2 (0, 1, 2, 4 in wave 1).",
+      "Monocyte draws 3 and 4 pass the time gate but decline less steeply than observed on the same paired test samples: slope ratio 0.68 and 0.70, slope bias +0.096 and +0.082 logit/day, p<0.001. Their bulk-only slopes (-0.160 and -0.172) are therefore attenuated estimates, and this explains most of the spread across draws.",
+      "The >21 d window is outside the paired calibration range: the latest paired sample is day 20, and all 16 bulk-only samples after day 21 are severe/critical. The step from d15-21 to >21 d is not significant (p 0.16-0.30). The window effect vs d0-7 (-2.4 to -3.7) is an uncalibrated plateau, not further measured decline.",
+      "Bulk-only subsets differ. The bulk-only visits of the 26 donors who also have paired samples fall about half as fast: -0.09 to -0.16, interaction p<0.03 in 4/5 draws. Those same donors' observed paired visits fall at -0.27. The difference comes from their bulk ISG score (-0.055 vs -0.119 SD/day; after adjusting for it the slope is about 0), so the generator is not at fault. But 'bulk-only matches observed' holds mainly for the donors with no single cells.",
+      "Bulk-only generated cells do not reproduce the steeper decline in severe disease. The observed paired cells show it (severity x day -0.142, p 0.006), paired-test generated cells show it in 5/5 draws, and Stephenson 2025 Fig. 4B reports it. In bulk-only cells the interaction is about 0 (p>=0.68). The bulk-only bulk score also lacks it (p 0.72) because severe bulk-only donors are first sampled later (median day 9 vs day 4). The claim must not be extended to severity-specific kinetics.",
+      "'Nothing beyond the bulk' is right for the time trend: after adjusting for the bulk score, slopes are -0.021 to -0.001 in bulk-only cells and -0.011 in observed cells. For cross-sectional levels, however, the generated fraction adds information beyond the 10-gene score in 4/5 draws (p 6e-7 to 2e-4). Limit the statement to the trajectory.",
+      "Shared noise. All draws used seed 20260927 and the same bulk input, so 5/5 agreement is not independent replication. The evidence rests on per-draw calibration gates, which the lens acknowledges.",
+      "Minor errors in the stated numbers:\n- Per-draw p ranges from 8e-13 (draw 1) to 5e-17, not 2e-13 to 5e-17.\n- The no-single-cell donor slopes are -0.19 to -0.28, outside the headline -0.16 to -0.25.\n- The observed monocyte n is 112 samples (113 is the T count).\n- Bulk-only COVID+ samples are 88% wave 1 (126/143; 87% within 21 d), not 89%.",
+      "Gisby 2022 (PDF p.8) is paraphrased loosely. Its SIGLEC1 sentence compares severe with mild/moderate patients at days 0-3. The time decline it reports is for plasma IFN proteins: IFNA10/13/16, IFNE and IFNL3 'elevated early ... and decreased over time'.",
+      "No archive delta: this verifier wrote only analysis files under wf/verify/IMP-LONG-1_calib. The PBISC-Diffusion maintenance update is left to the coordinating agent."
+     ],
+     "reasoning": "Core result: confirmed. For ISG-high monocytes all four DATA_GUIDE honesty checks pass in all five draws.\n\n1. Calibration\n- calibration.csv: ratio 0.65-0.81, Spearman 0.78-0.85. The COVID+ vs COVID- and severity contrasts have the same sign in 5/5 draws.\n- calibration.csv has no time contrast, so I recomputed one on COVID+ paired test samples (depth-matched observed_thin_d<j>):\n  - level and within-donor tracking hold in every draw;\n  - the generated decline has the observed sign in every draw, with slope ratio 0.68-0.99 (0.72-1.00 in wave 1);\n  - draws 3 and 4 decline about 30% less steeply than observed.\n- No test sample belongs to a donor that trained its draw.\n- Calibration transfers to bulk-only samples. The generator's map from bulk ISG score to generated ISG-high share is the same on paired and bulk-only samples, and the ISG bulk genes do not differ between the two sets within any window.\n- The trend is not a depth artefact (depth-equalised calls and pseudobulk ISG both decline). It survives onset-based time, demographic adjustment, within-donor slopes, donor-level slopes and a donor bootstrap.\n\n2. Observed paired cells\n- The same decline is strongly present: slope -0.282, p 5e-28; 84% of donor slopes are negative.\n- Wave 1 -0.222, wave 2 -0.340. Bulk-only wave-1 slopes are 0.76-1.15x the observed wave-1 slope.\n- The steeper wave-2 decline fits Stephenson 2025's report of glucocorticoid-treated monocytes in the 2021 cohort (PDF p.14).\n\n3. Biology and novelty\n- Already reported for this cohort, so this is a positive control, not a discovery. Stephenson 2025 Cell Genomics:\n  - PDF p.9 (journal p.8, Fig. 4A): IFN-stimulated cell subtypes 'were significantly increased within the first week following symptom onset and then gradually reduced over time';\n  - same page, Fig. 4B: a steeper decline in severe disease;\n  - PDF p.14 (journal p.13): 'a peak in cells showing an IFN-activated gene signature in the first week of illness followed by a waning';\n  - Highlights (p.1): 'Interferon response gene upregulation occurs early'.\n- Gisby 2022 Nat Commun PDF p.8: plasma type I/III IFN proteins rise early and then fall; SIGLEC1 is higher in severe disease at days 0-3.\n\n4. Beyond the bulk\n- The bulk ISG score tracks the observed fraction better than the generated cells do.\n- It removes the time effect completely in the observed cells and in every draw.\n\nWhy qualified rather than confirmed:\n- The T-cell extension is calibrated only in draws 0-2 (0, 1, 2, 4 in wave 1). Draw 3's ISG-high T level is 0.29x observed, which fails the guide's trust rule, yet its slope is part of the claimed T range.\n- Draws 3 and 4 understate the decline.\n- The >21 d window is uncalibrated extrapolation.\n- The bulk-only visits of donors who also have single cells fall half as fast. This comes from their bulk, not the generator.\n- The bulk-only set cannot show the severity-specific steepening that the observed cells and the cohort paper report, because of how it was sampled.\n\nNothing contradicts the main claim, so it is not refuted. It should be reported as an ISG-high monocyte positive control (all draws), with T cells limited to the calibrated draws and the numeric fixes listed above.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/run.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/vutil.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v01_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v01_calibration.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v01_calibration_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v01_temporal_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v02_bulkonly.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v02_bulkonly.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v02_bulkonly_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v02_bulkonly_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v02_wave1_bh_allfeatures.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v03_observed.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v03_observed.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v03_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_bulk_beyond.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_bulk_beyond.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_a_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_b_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_c_score_trajectory.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_d_transfer_map.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v04_d_transfer_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v05_depth.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v05_depth.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v05_depth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v06_pdf_text.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v07_time_covariates.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v07_time_covariates.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v07_time_covariates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v07_covneg_levels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v08_shape_sev.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v08_shape_sev.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v08_window_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v08_sevxday_bulkscore.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v09_training_donors.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v09_training_donors.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v10_thin_factors.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-1_calib/v10_thin_factors.log",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-POP-4",
+   "lens": "imp_populations",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 3,
+    "theme": "interferon (positive control)",
+    "synthesis_strength": "strong positive control in myeloid cells (draws 0-4); lymphoid states only in draws 0-2; severity sub-claim too strong",
+    "reportable_statement": "Generated cells of bulk-only COVID+ samples carry larger ISG-high (>=4 of 10 ISGs) shares than COVID- samples in CD14 monocytes (+0.171 to +0.200; 0.23-0.26 vs 0.03-0.08), CD16 monocytes (+0.159 to +0.190), NK (+0.055 to +0.081), non-plasma B (+0.042 to +0.065), ISG+ cytotoxic T (+0.021 to +0.049) and DC (+0.136 to +0.176), as observed paired cells do. Positive control; nothing beyond the bulk.",
+    "carrying_draws": {
+     "CD14, CD16, DC (embedding-free rules)": "0-4",
+     "cDC2 (label rule)": "0,1,3,4",
+     "NK, non-plasma B, ISG+ cytotoxic T": "0-2 (ratios 0.50-0.71, near the gate; with per-cell depth equalisation T only 2, B 1-2)"
+    },
+    "honesty": {
+     "calibration": "CD14 ratio 0.60-0.78, Spearman 0.74-0.83; CD16 0.58-0.78; NK 0.37-0.70; B 0.36-0.71; T 0.23-0.72; DC 0.62-0.79; COVID signs 5/5 in every lineage. On wave-1 tests T ISG+ cytotoxic is over-produced (1.25-1.42x in draws 0, 2, 4).",
+     "replication": "5/5 draws in every lineage (q <= 0.011 over 9 ISG rules). Survives sex flag, training donors, wave 1 only, donors on both sides, label confidence >=0.9, capped or equalised per-cell depth, and rank-ANCOVA for age, sex, wave, ethnicity, diabetes and bulk depth. Shared noise: same-slot OR 42-94 vs 7-16 shifted.",
+     "observed": "Observed CD14 +0.365 (p 1.2e-6), CD16 +0.382, NK +0.227, B +0.187, T +0.122, DC +0.344, cDC2 +0.353; within donors 9/9 to 11/11 rise from pre-infection to infection. Observed severity effects are strong over both waves, but in wave 1 only monocytes and DC are significant (NK p 0.097, B 0.135, T 0.067) unless restricted to <=14 days.",
+     "beyond_bulk": "None: fractions correlate with bulk ISG10 (0.68-0.88); bulk ISG10 predicts observed fractions better (0.80-0.92 vs 0.69-0.88)."
+    },
+    "verdict_reasons": [
+     "'Severity reproduced only in monocytes, not in NK, B or T' is too strong: within <=10 days NK and T ISG+ cytotoxic rise with severity in 5/5 draws (gated draws p 0.014-0.036) and B in 3/5; the DC rule gives 4/5 after adjustment. Myeloid is robust; lymphoid is attenuated and window-sensitive.",
+     "Lymphoid calibration is borderline and depends on the thinning method.",
+     "cDC2 holds in 4/5 draws, so 'every lineage' is overstated; MAIT and pDC untestable.",
+     "Count errors: T severity 1/5 (draw 4), NK fatal 3/5, adjusted CD14 fatal 2/5.",
+     "A COVID- control taken 7 days before the first positive swab (C105_negative; highest bulk ISG of all COVID- samples) makes mean-scale contrasts conservative.",
+     "Combes 2021 found the opposite severity direction in non-ESKD cohorts; the claim cited it only as describing ISG states."
+    ],
+    "refuted_subclaims": [
+     "severity contrast not reproduced in NK, B or T"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 PDF p.4, Fig. 4 (PDF p.8), p.9; Gisby 2022 pp.7-8 and p.13. Positive control.",
+    "next_steps": [
+     "Report lymphoid ISG states only in draws 0-2, with depth-matched rules.",
+     "State severity contrasts within a fixed illness window (<=10-14 days).",
+     "Add ISG-high NK, B and DC rows to the trust layer."
+    ],
+    "key_sources": [
+     "verify/IMP-POP-4_recompute/tables/v09_summary.csv",
+     "verify/IMP-POP-4_recompute/tables/v02_calibration.csv",
+     "verify/IMP-POP-4_recompute/tables/v06_severity_timing.csv",
+     "verify/IMP-POP-4_recompute/tables/v04_shared_noise.csv",
+     "verify/IMP-POP-4_calib/tables/v03_summary.csv",
+     "verify/IMP-POP-4_calib/tables/v02_calibration.csv",
+     "verify/IMP-POP-4_calib/tables/v02_transfer.csv",
+     "verify/IMP-POP-4_calib/tables/v04_fatal.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-POP-4",
+    "cohort": "imperial_eskd",
+    "claim": "Generated cells of bulk-only COVID+ samples carry IFN-stimulated (ISG-high) sub-states in every lineage at higher shares than COVID- samples: CD14 and CD16 monocytes, NK, non-plasma B, cytotoxic T and cDC2. The observed paired cells show the same. This is the cohort's positive control. The severity contrast is reproduced only in monocytes; it is not reproduced in NK, B or T cells.",
+    "kind": "state",
+    "feature": "ISG-high share (>=4 of 10 ISGs) within CD14 monocytes, CD16 monocytes, NK and non-plasma B labels. ISG+ cytotoxic share of T cells (ISG-high plus >=2 of GZMH, GZMB, PRF1, NKG7, GNLY). Joint-embedding counterparts: Mono r1.0 c0/c1, NK c0, B c0, T c10, DC c4 (Jaccard 0.55-0.76 on independent re-embedding). Markers are concordant in all of them (rho 0.72-0.94).",
+    "comparison": "COVID+ vs COVID- (58 vs 19 donors); also severe vs mild and fatal vs non-fatal among COVID+.",
+    "effect_bulkonly": "COVID+ vs COVID- by lineage. CD14: 0.23-0.26 vs 0.03-0.08 (+0.171 to +0.200). CD16: +0.159 to +0.190. NK: 0.073-0.102 vs 0.017-0.026 (+0.055 to +0.081). B: +0.042 to +0.065. T ISG+ cytotoxic: +0.021 to +0.049. cDC2 cluster: +0.07 to +0.14. Severity: CD14 +0.081 to +0.145 (3/5 p<0.05, adjusted 4/5); CD16 2/5; NK, B and T 0/5 (+0.003 to +0.044). Fatal: CD14 5/5, T 4/5, B 4/5, NK 2/5 p<0.05. Adjusted for days since swab, the fatal effects mostly vanish outside CD14.",
+    "stats": "Donor-level Mann-Whitney per draw; BH over the 17 rules. COVID p<=0.0088 (CD14), <=0.0054 (CD16), <=0.0037 (NK), <=0.0033 (B), <=0.0018 (T) in all 5 draws; q<=0.017. Severity models: donor-clustered OLS with days since swab, age, sex and wave.",
+    "draws_same_sign": "5/5 for COVID in every lineage. Draws that pass the gate: CD14 and CD16 0-4; NK, B and T 0-2 only. Draws 3-4 under-produce these states (ratio 0.23-0.49). Shared sampler noise: same-slot OR 52.6 vs 7.6 for CD14 ISG-high.",
+    "paired_calibration": "CD14: ratio 0.60-0.78, Spearman 0.71-0.83, COVID contrast generated +0.21 to +0.30 vs observed +0.29 to +0.38. CD16: ratio 0.59-0.78, Spearman 0.71-0.76. NK: 0.37-0.70, 0.72-0.79. B: 0.36-0.71, 0.70-0.77. T ISG+ cytotoxic: 0.23-0.72, 0.68-0.78. Contrast signs agree 5/5 in every lineage. calibration.csv rows: 'ISG-high monocyte' ratio 0.65-0.81, Spearman 0.78-0.85, COVID and severity 5/5; 'ISG-high T' ratio 0.29-0.60, Spearman 0.72-0.81, 5/5.",
+    "paired_observed_concordance": "Yes. Observed native cells, COVID: CD14 +0.365 (p=1.2e-6), CD16 +0.382 (2e-6), NK +0.227 (3e-7), B +0.187 (6e-6), T ISG+ cytotoxic +0.122 (3e-6). Observed severity is strong in every lineage: CD14 +0.386 (p=0.0023), CD16 +0.367 (0.0053), NK +0.268 (0.0013), B +0.238 (0.0021), T +0.202 (0.0029). Its weak reproduction in bulk-only NK, B and T fits the timing: bulk-only severe samples were taken later (median 15 vs 9 days since swab) and have lower bulk ISG than paired severe samples (-0.89 log2, p=0.041; s08_transfer). Bulk-only and paired bulk profiles do not differ in COVID- or mild/moderate donors.",
+    "beyond_bulk": "None. Fractions correlate with bulk ISG10 (Spearman 0.68-0.90). The CD14 ISG-high fraction adds nothing to bulk baselines for severity or fatal outcome (LR p 0.16-0.99). The observed T ISG+ cytotoxic fraction adds to bulk for severity (p=0.046), but the generated fraction does not (0.64-0.99).",
+    "novelty": "Reported. Stephenson 2025 Fig 4A (p.7) shows IFN-stimulated states of monocytes, DC, NK, B and T cells rising in week 1, and a greater early IFN response in severe disease (p.8). Combes 2021 describes ISG-high states in COVID. It serves as the positive control here.",
+    "strength": "strong",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s07_rules.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_results.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07b_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s15_finding_numbers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s04_summary_r1.0.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_spearman.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_transfer.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08b_increment_extended.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s06_stability.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a9322bb29a3b25082",
+     "verdict": "qualified",
+     "recomputed": "All numbers below come from my own code (vlib.py, v01-v09), run on the raw cells.parquet and markers.npz files. I used the same rules: >=4 of 10 ISGs; T also needs >=2 of GZMH/GZMB/PRF1/NKG7/GNLY; parents need >=20 cells. Tests are donor-level Mann-Whitney within each draw. Generated native fractions match the discovery agent's values exactly.\n\nCOVID+ vs COVID-, bulk-only, by draw:\n- CD14: +0.171 to +0.200, p 0.0003-0.0088. Level 0.23-0.26 vs 0.03-0.08.\n- CD16: +0.159 to +0.190, p<=0.0054.\n- NK: +0.055 to +0.081, p<=0.0037.\n- B (non-plasma): +0.042 to +0.065, p<=0.0033.\n- T ISG+ cytotoxic: +0.021 to +0.049, p<=0.0018.\n- Every one of these holds in 5/5 draws. BH over 9 rules gives q<=0.0099.\n- cDC2, a label-based rule I added (AIFI L2 cDC2): +0.128 to +0.195, significant in 4/5 draws. Draw 2 is not significant (p=0.24).\n\nRobustness of the COVID contrast. For CD14, CD16, NK, B and T, all 8 variants give p<0.05 in every draw (largest p 0.027). The variants are:\n- dropping the sex-flag sample and samples whose donor trained the draw;\n- wave 1 only;\n- dropping the 3 donors with samples on both sides;\n- the strict combination of these;\n- labels with confidence >=0.9;\n- per-cell depth capped;\n- per-cell depth equalised (fixL). Effects there: CD14 +0.118 to +0.180, NK +0.039 to +0.078, B +0.034 to +0.053, T +0.014 to +0.042.\n\nCovariate adjustment:\n- Rank-ANCOVA for age, sex and wave: p<=0.025 in all 25 lineage-by-draw cells.\n- Adding diabetes: all cells still significant except CD16 draws 1 and 2 (p 0.069 and 0.106).\n- Adding ethnicity or bulk sequencing depth changes nothing.\n- On the mean scale, adjusted OLS is weak for B, T and CD16, but every p is <=0.005 once the COVID- sample C105_negative is dropped.\n- Generated COVID+ cells are shallower than COVID- cells. For example, T median library 996-1466 vs 1116-1587. So depth works against the finding.\n\nCalibration (paired_gen vs observed thinned to the draw, my own seed):\n- CD14: ratio 0.60-0.78, Spearman 0.74-0.83, gate passes in draws 0-4.\n- CD16: ratio 0.58-0.77, gate passes in draws 0-4.\n- NK: ratio 0.37-0.70. B: 0.36-0.71. T: 0.23-0.72. For all three the gate passes only in draws 0-2; T draw 1 sits right at the cut-off (ratio 0.504).\n- With per-cell depth equalisation (fixL), T passes only draw 2 and B passes only draws 1-2.\n- calibration.csv rows match my recomputation: 'ISG-high monocyte' ratio 0.65-0.81, Spearman 0.78-0.85; 'ISG-high T' ratio 0.29-0.60, Spearman 0.72-0.81; 5/5 sign agreement for both.\n\nObserved paired cells match the claim exactly. COVID: CD14 +0.365 (p 1.2e-6), CD16 +0.382, NK +0.227, B +0.187, T +0.122. Severity: CD14 +0.386 (p 0.0023), CD16 +0.367, NK +0.268, B +0.238, T +0.202. Restricted to wave 1, the observed severity effect is significant in CD14 only (+0.40, p 0.012). The other wave-1 p-values are NK 0.097, B 0.135, T 0.067 and CD16 0.21.\n\nSeverity in bulk-only, unadjusted, counting draws with p<0.05:\n- CD14 3/5, CD16 2/5, NK 0/5, B 0/5.\n- T 1/5: draw 4, p=0.041.\n- Adjusted for days, age, sex and wave (mean scale): CD14 4/5.\n\nSeverity in samples taken <=10 days after the swab:\n- CD14 5/5.\n- NK 5/5 (p 0.013-0.022; draws 0-2 have p=0.017 each).\n- T 5/5 (p 0.003-0.036).\n- B 3/5 (draw 0 p=0.014, draw 1 p=0.020).\n- CD16 3/5.\n\nOther severity checks:\n- In the <=14-day window: NK 1/5, B 1/5, T 3/5.\n- Donor rank-ANCOVA with days: CD14 5/5, NK 3/5, B 3/5, T 3/5.\n- Paired calibration in the <=10-day window: generated and observed contrasts have the same sign in 30/30 rule-by-draw cells.\n\nFatal, p<0.05: CD14 5/5, T 4/5, B 4/5, NK 3/5. After the days adjustment (mean scale), CD14 holds in 2/5.\n\nBulk-level checks:\n- Bulk ISG10, severe donors: bulk-only is 0.886 lower than paired (p 0.041), with median days 15 vs 9.\n- No difference for COVID- (p 0.39) or mild/moderate donors (p 0.89).\n- Spearman of the generated fractions with bulk ISG10: 0.68-0.84.\n- Adding the generated fraction to bulk ISG10 for COVID status: LR p 0.09-0.97.\n- For severity or fatal, with days in the model: no positive added value.\n\nShared sampler noise. Same-slot odds ratios are 42-94, against 7-16 for shuffled slots (CD14: 49.2 vs 8.2). The within-sample residual correlation of the cell calls is 0.43-0.50.\n\nWithin-donor checks. C101 and C33 have COVID+ above COVID- in every draw. C105 goes the other way in every draw. Its COVID- sample was taken 7 days before the first positive swab and has bulk ISG10 9.37, the highest of all 19 COVID- samples (COVID+ median 6.63).",
+     "issues": [
+      "The claim says severity is 'not reproduced in NK, B or T cells'. That is too strong. Bulk-only severe samples were taken later (median 15 vs 9.5 days), which hides the effect. In samples taken <=10 days after the swab, severe beats mild in NK in 5/5 draws (gated draws 0-2: p=0.017 each), in T ISG+ cytotoxic in 5/5 (gated draws: p=0.029, 0.014, 0.036) and in B in 3/5 (gated draws 0-1). A donor rank-ANCOVA with days gives 3/5 for each. The paired early-window calibration agrees in sign in every draw. The lymphocyte severity effect is time-confounded, not absent. It is sensitive to the window: in the <=14-day window NK reaches 1/5, B 1/5 and T 3/5.",
+      "Lymphocyte calibration is borderline. In the gated draws 0-2, NK, B and T ratios are 0.50-0.71 (T draw 1 = 0.504, B draws 0-1 = 0.54). With per-cell depth-equalised thinning (fixL), T passes only draw 2 and B only draws 1-2. Draws 3-4 fail (0.23-0.47). Which draws pass the gate depends on the thinning method, so the NK, B and T results rest on 1-3 draws.",
+      "In paired cells restricted to wave 1 (which holds all bulk-only COVID- and 88% of COVID+ samples), the observed effects are smaller. The COVID effect for T ISG+ cytotoxic is +0.034, against +0.122 in all waves. The severity effect is significant only in monocytes (NK p=0.097, B 0.135, T 0.067). The claim's 'observed severity is strong in every lineage' depends on wave 2. Against wave-1 observed cells, the generated T ISG+ cytotoxic ratio is 1.24-1.52 in draws 0, 2 and 4, so the reported 'under-production' depends on the population.",
+      "cDC2 is listed among 'every lineage' but has no per-draw statistics. With an embedding-free label rule (AIFI L2 cDC2), the COVID effect holds in 4/5 draws (draw 2 p=0.24) and in 3/5 after rank-ANCOVA adjustment.",
+      "Several small count errors: T severity is 1/5 at p<0.05 (draw 4, p=0.041), not 0/5. NK fatal is 3/5 at p<0.05, not 2/5. After the days adjustment, CD14 fatal is 2/5 with the discovery's own mean-scale model, so 'the fatal effects mostly vanish outside CD14' overstates CD14 (it is 4/5 with a rank model). Spearman with bulk ISG10 is 0.68-0.84, not up to 0.90. Severity q-values depend on the BH family: none of the 5 CD14 draws reaches q<0.05 over my 9 ISG rules.",
+      "The fractions are very skewed. Many COVID+ donors are near 0 (B median 0.014), and a minority drive the mean. One COVID- sample, C105_negative, was taken 7 days before the first positive swab and has the highest bulk ISG of all COVID- samples. Because of both, mean-scale adjusted OLS is not significant for B, T and CD16. Rank-ANCOVA, and any model run without C105_negative, keeps the effect. The mislabelled control makes the reported contrast conservative.",
+      "Shared sampler noise is strong (same-slot OR 42-94 vs 7-16 for shuffled slots; residual call correlation 0.43-0.50), so '5/5 draws' is not five independent replications. The COVID effect does not depend on this: each gated draw is individually significant.",
+      "The finding adds nothing beyond the bulk: the generated fractions add no positive information to bulk ISG10 for COVID status, severity or fatal outcome. Observed T ISG+ cytotoxic does add for fatal outcome (p=0.0006)."
+     ],
+     "reasoning": "I recomputed every feature independently from the raw compact files. I used my own loaders, my own thinning seed, and two depth-control variants of my own: per-cell depth capped (capL) and per-cell depth equalised (fixL). Every native generated number in the claim reproduces exactly. The calibration values reproduce within thinning noise, and so do the observed truth values and the bulk transfer numbers.\n\nThe core claim is the positive control: COVID+ bulk-only samples carry higher ISG-high shares in CD14 and CD16 monocytes, NK cells, non-plasma B cells and cytotoxic T cells. It holds in 5/5 draws and survives every confounder control I tried:\n- dropping the sex-flag sample and donor-trained samples;\n- wave 1 only (all COVID- samples are wave 1);\n- dropping the three donors with samples on both sides;\n- labels with confidence >=0.9;\n- per-cell depth equalisation (generated COVID+ cells are in fact shallower, which works against the finding);\n- ratio-of-sums pooling;\n- rank-ANCOVA for age, sex, wave, ethnicity, diabetes and bulk depth.\nIt also matches the observed paired cells and the published biology.\n\nWhich draws carry the result:\n- CD14 and CD16: draws 0-4.\n- NK, B and T: only draws 0-2 pass the gate, with ratios near the 0.5 cut-off and sensitive to the thinning method. Under fixL, T passes only draw 2 and B only draws 1-2.\n- cDC2, using my label-based rule: draws 0, 1, 3 and 4.\n\nI rate the claim qualified, not confirmed, for these reasons:\n- Its secondary negative statement, that severity is reproduced only in monocytes and not in NK, B or T, does not survive a timing control. Within <=10 days of the swab, the generated severity contrast appears in NK and T in 5/5 draws, including all gated draws, and in B in 3/5. The paired calibration in the same window agrees in sign everywhere. So the lymphocyte severity effect is hidden by later sampling of bulk-only severe donors, not absent. It is, however, sensitive to the window chosen.\n- Lymphocyte calibration is weak and depends on the method.\n- cDC2 holds in only 4/5 draws.\n- Several per-draw counts are slightly wrong.\n- The finding says nothing beyond the bulk.\n\nNo GPU was used. I wrote only under my lens directory. I made no paper-archive update; that is left to the coordinating agent.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v01_fractions.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v02_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v03_adjusted.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v04_shared_noise.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v05_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v06_severity_timing.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v07_increment.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v08_calib_early.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/v09_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v09_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v01_fracs.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v01_depth_targets.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v02_bulkonly_tests.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v02_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v02_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v02_diagnostics.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v02_within_donor.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v03_adjusted_covid.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v03_adjusted_severity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v04_shared_noise.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v05_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v05_bulk_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v05_spearman_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v05_increment_covid.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v06_severity_timing.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v07_increment.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/tables/v08_calib_early.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/logs/v02_tests.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/logs/v03_adjusted.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_recompute/logs/v06_severity_timing.log"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a02704f7a7c4ca436",
+     "verdict": "qualified",
+     "recomputed": "Independent re-implementation (own code, own thinning seeds). Verifier scripts and tables are in wf/verify/IMP-POP-4_calib/. All numbers below are per draw d0..d4.\n\nCOVID+ vs COVID- calibration on the paired test samples (paired_gen against observed thinned globally):\n- CD14 ISG-high: ratio 0.65/0.69/0.78/0.60/0.63; rho 0.82/0.73/0.79/0.80/0.76; gen diff +0.30/+0.24/+0.29/+0.21/+0.22 vs obs +0.37/+0.30/+0.30/+0.29/+0.32. Signs agree 5/5. Gate passes in d0-4.\n- CD16: ratio 0.59-0.78, rho 0.72-0.77. Gate passes in d0-4.\n- NK: ratio 0.55/0.54/0.69/0.37/0.42. Gate passes in d0-2 only.\n- B (non-plasma): ratio 0.54/0.54/0.71/0.36/0.39. Gate passes in d0-2.\n- T ISG+ cytotoxic: ratio 0.69/0.51/0.73/0.23/0.48. Gate passes in d0-2.\n- DC ISG-high (embedding-free rule; the candidate gave none): ratio 0.62-0.79, rho 0.73-0.77. Gate passes in d0-4.\n- cDC2 ISG-high (embedding-free rule): ratio 0.54-0.73, rho 0.69-0.73. Gate passes in d0-4.\n- Robustness: the gates are the same with per-sample thinning and with depth-equalised sets. On wave-1-only test samples the signs are 5/5 everywhere, but T ISG+ cytotoxic is over-produced (ratio 1.42/1.25/1.25 in d0/d2/d4).\n- Severity calibration (COVID+ test samples): signs agree 5/5 in every lineage. The generated/observed contrast ratio is 0.59-0.85 in myeloid cells and 0.21-0.72 in lymphoid cells.\n\nBulk-only COVID+ vs COVID- (58 vs 19 donors; donor-level Mann-Whitney):\n- CD14 +0.200/+0.196/+0.178/+0.180/+0.171 (p<=0.0088)\n- CD16 +0.159 to +0.190 (p<=0.0054)\n- NK +0.055 to +0.081 (p<=0.0037)\n- B +0.042 to +0.065 (p<=0.0033)\n- T ISG+ cytotoxic +0.021 to +0.049 (p<=0.0018)\n- BH q<=0.011 over 9 ISG rules.\n- DC ISG-high +0.136 to +0.176 (5/5 draws, p<=0.027). cDC2 ISG-high +0.127 to +0.195 (4/5; d2 p=0.24).\n- The candidate's DC cluster c4 at resolution 1.0 gives +0.061 to +0.152, with d3 p=0.19.\n- All of these survive four checks: one unit per donor, removing the sex-flag sample and training-donor samples, and depth equalisation.\n- Generated COVID+ parent libraries are equal or lower than COVID- ones (NK d1-4 lower, p<=0.04), so depth works against the contrast rather than creating it.\n\nBulk-only severity, all time points (28 vs 30 donors):\n- Nominal p<0.05 in: CD14 d0/d3/d4; CD16 d3/d4; DC ISG-high d3/d4; cDC2 d3/d4; NK 0/5; B 0/5; T ISG+ cytotoxic d4 only (p=0.041, an ungated draw).\n- After adjusting for days, age, sex and wave: CD14 4/5; CD16 2/5; DC ISG-high 4/5 (p=0.039, 0.020, 0.064, 0.0001, 0.002); cDC2 4/5; NK, B and T 0/5.\n- Severity within 14 days of the positive swab (about 23 vs 30 donors): CD14 5/5; DC 4/5; CD16 3/5; NK d0 (+0.090, p=0.034); T ISG+ cytotoxic d0/d3/d4 (d0: +0.055, p=0.034); B d4. Every lineage is positive in 5/5 draws.\n\nFatal vs non-fatal:\n- Unadjusted p<0.05: CD14 5/5, CD16 3/5, NK 3/5, B 4/5, T 4/5, DC 5/5.\n- Adjusted: CD14 2/5, CD16 2/5, DC 2/5, cDC2 3/5, NK/B/T 0/5.\n\nObserved paired cells (native):\n- COVID: CD14 +0.365 (p=1.2e-6), CD16 +0.382, NK +0.227, B +0.187, T +0.122, DC +0.344, cDC2 +0.353.\n- Within-donor, pre-infection vs infection: CD14 10/10 donors up (p=0.002), CD16 11/11, NK 11/12, B 9/9, T 11/12, DC 11/11.\n- Severity (all samples): CD14 +0.386 (p=0.0023), NK +0.268, B +0.238, T +0.202, DC +0.334 (p=0.001).\n- Wave-1 only: CD14 p=0.012, DC p=0.008, but NK p=0.097, B p=0.135, T p=0.067. Wave-1 within 14 days: NK p=0.024, B p=0.032, T p=0.0047.\n\nDoes the calibration transfer (paired vs bulk-only bulk profiles)?\n- Bulk ISG10, bulk-only minus paired: COVID- +0.20 (p=0.39); mild/moderate +0.08 (p=0.89); severe/critical -0.89 log2 (p=0.041).\n- Days since swab in severe donors: 13.4 vs 9.3 (p=0.043); sample medians 15 vs 9 days.\n- Bulk ISG10 severity difference: bulk-only +0.67 (p=0.10) vs paired +1.63 (p=0.001). Within 14 days: +1.19 (p=0.013) vs +1.77 (p=0.005).\n\nInformation beyond the bulk:\n- Spearman of generated fraction with bulk ISG10: 0.68-0.88.\n- On paired test samples, bulk ISG10 predicts the observed fractions better (rho 0.80-0.92) than the generated fractions do (rho 0.69-0.88). Partial rho of generated with observed, given ISG10, is 0.00-0.38.\n- Severity likelihood-ratio increment over ISG10 plus days: p=0.05-0.98 for generated fractions and p=0.095-0.86 for observed fractions.\n\nShared sampler noise (median same-slot vs shifted-slot odds ratio across draw pairs): CD14 52.6 vs 7.6, NK 57.8 vs 7.9, B 73.1 vs 10.0, T 89.7 vs 13.1.\n\nShape of the ISG distribution: generated COVID+ cells keep a high-ISG tail. CD14 cells with 7 or more ISGs: d0 0.105 generated vs 0.175 observed. Strict-to-rule ratios are similar in d0-2 and compressed in d3-4.",
+     "issues": [
+      "Severity is not 'reproduced only in monocytes'. The embedding-free DC ISG-high rule shows the same severity pattern as CD16 monocytes. It passes the paired gate in all 5 draws, with sign 5/5 and contrast ratio 0.68-1.03. Bulk-only: adjusted p<0.05 in 4/5 draws, within 14 days 4/5, unadjusted 2/5. Observed DC: +0.334, p=0.001. The supported statement is that severity is reproduced in myeloid cells (monocytes and DCs).",
+      "'Not reproduced in NK, B or T' is too strong. In bulk-only the lymphoid severity difference is positive in 5/5 draws for every lineage, and the paired calibration reproduces it at 0.2-0.7x. Within 14 days of the swab, where the observed lymphoid effect sits, gated draw d0 is nominally significant for NK (+0.090, p=0.034) and for T ISG+ cytotoxic (+0.055, p=0.034). The ungated draws d3/d4 are also significant for T. The effect is attenuated and underpowered, not absent.",
+      "Several counts are wrong. Severity for T is 1/5 at p<0.05 (d4, p=0.041), not 0/5. NK fatal is 3/5 (p=0.020, 0.027, 0.0034), not 2/5. After adjustment, the CD14 fatal effect survives in only 2/5 draws, the same as CD16 (2/5) and DC (2/5), so 'mostly vanish outside CD14' overstates CD14. The cDC2 cluster c4 COVID effect is +0.06 to +0.15 at resolution 1.0 (d3 p=0.19), not +0.07 to +0.14.",
+      "Bulk-only CD14 severity is only nominal. BH over the 9 ISG rules gives q=0.06-0.40; it reaches q<0.05 (d3, d4) only in the candidate's 17-rule family, where small HLA-rule p-values help the correction. Adjusted 4/5 and within-14-days 5/5 (q<0.05 in 3/5) are more robust.",
+      "The paired calibration population is mostly wave-2 COVID+ (62 of 113 COVID+ samples), while bulk-only is 126 of 143 wave-1 samples. In wave-1-only test samples the COVID signs still agree 5/5, but T ISG+ cytotoxic is over-produced (ratio 1.25-1.42 in d0/d2/d4). Observed wave-1 lymphoid severity is not significant overall (NK p=0.097, B 0.135, T 0.067) and is significant only within 14 days. So the strong observed lymphoid severity effect the candidate quotes is driven by wave 2, where all 7 severe donors died.",
+      "Novelty: the candidate cites Combes 2021 only as describing ISG-high states in COVID. Its severity direction is the opposite of this cohort's. The preprint abstract (PDF p.2) says mild patients show ISG expression 'across every cell population and these cells are systemically absent in patients with severe disease'; PDF p.4 says the ISG monocytes, T and B cells are enriched 'specifically in mild/moderate' disease. The finding is already reported for this cohort: Stephenson 2025, journal pp.3 and 8 with Fig 4 on p.7, and the bulk paper Gisby 2022, pp.7-8 and p.13.",
+      "Draw agreement ('5/5') is not five independent replications. Shared sampler noise couples the state calls across draws even more strongly in lymphoid rules than in CD14 (same-slot odds ratio 57.8-89.7 vs 7.6-13.1 shifted). Only the per-draw gates, d0-4 myeloid and d0-2 lymphoid, should carry the claim.",
+      "Nothing beyond the bulk, confirmed. Bulk ISG10 alone predicts the observed lineage-level fractions better than the generated fractions in every rule and draw. There is no severity increment over ISG10 plus days. The generated lymphoid fractions re-express the bulk ISG signal, with only a small residual partial correlation (<=0.38)."
+     ],
+     "reasoning": "Main claim: in generated cells of bulk-only samples, COVID+ samples have higher ISG-high shares than COVID- samples in every lineage. This is the cohort's positive control, and it holds on every axis.\n\n(1) Calibration. The feature is in calibration.csv only as 'ISG-high monocyte' and 'ISG-high T': COVID and severity signs agree 5/5, ratio 0.65-0.81 and 0.29-0.60. For the five lineage rules plus DC and cDC2, I recomputed calibration with my own code and thinning seeds, in three depth-matching modes (global, per-sample, depth-equalised) and two populations (all test samples, wave-1 only).\n- COVID signs agree 5/5 for every rule.\n- Gates pass in d0-4 for CD14, CD16, DC and cDC2, and in d0-2 for NK, B and T ISG+ cytotoxic (draws 3-4 under-produce at 0.23-0.49x and compress the high-ISG tail).\n- In bulk-only, the COVID contrast is significant in every draw for every claimed lineage (q<=0.011). It survives independent donor units, removal of the sex-flag sample and training-donor samples, and depth equalisation. Generated COVID+ libraries are, if anything, shallower, so depth works against the contrast.\n\n(2) Observed cells. Strongly supportive, including a within-donor pre-infection vs infection test: 9/9 to 11/11 donors increase in every lineage.\n\n(3) Biology. Already reported for this cohort.\n- Stephenson 2025: the IFN-α/β response is enriched across innate and adaptive cells (journal p.3); IFN-stimulated subtypes rise in week 1, with CD14/CD16 monocytes, DC3, NK, switched-memory B and CD8 EM persisting into weeks 2-3 (p.8, Fig 4 on p.7); severe disease has a higher early IFN response with a steeper decline (p.8; Highlights). Journal page = PDF page minus 1.\n- Gisby 2022 (the bulk half of the same cohort): SIGLEC1 and the type I IFN response are stronger and earlier in severe disease (pp.7-8, p.13).\n- So this is a positive control with no novelty.\n\n(4) Beyond the bulk. None; confirmed with partial correlations and likelihood-ratio tests.\n\nThe secondary severity statements are only partly supported:\n- 'Only in monocytes' is contradicted by the embedding-free DC rule.\n- 'Not in NK/B/T' is too strong: the lymphoid direction is positive in 5/5 draws and nominally significant in gated d0 within 14 days. The weakness is attenuation in the model, plus later sampling and lower bulk ISG in bulk-only severe donors. My transfer analysis confirms the candidate's timing explanation.\n- Several counts are wrong: T severity 1/5, NK fatal 3/5, and adjusted CD14 fatal only 2/5.\n- The novelty statement leaves out that Combes 2021 found the opposite severity direction.\n\nVerdict: qualified. The COVID positive control is confirmed in the gated draws (CD14/CD16/DC d0-4; NK/B/T d0-2). The severity part must be restated as reproduced in myeloid cells and attenuated, not absent, in lymphoid cells, and the counts corrected.\n\nProcess notes: CPU only, run under systemd-run with MemoryMax=6G and MemorySwapMax=0. No GPU was used. Nothing was written outside the verifier directory. No PBISC-Diffusion archive delta was published; that is the coordinating agent's decision if these tables become paper evidence.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-4_calib/tables/v02_increment.csv",
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+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Combes2021_Absence_protective_ISG_states_severe_COVID19_preprint.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-PROG-1",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 4,
+    "theme": "monocyte HLA class II",
+    "synthesis_strength": "moderate for severity (CD14 and intermediate monocytes, cDC2; draws 0-4); weak for COVID and fatal; B-cell sparing not calibrated",
+    "reportable_statement": "In bulk-only severe/critical patients the generated CD14 and intermediate monocytes (and cDC2) express less HLA class II than in mild/moderate patients (CD14 -0.24 to -0.39 log2; intermediate -0.21 to -0.42), consistent with observed paired cells (CD14 -0.58, p 0.001; wave 1 -0.70, p 0.02). COVID+ vs COVID- and fatal show the same direction but are weaker: part of the CD14 effect rides on the generator's spurious CD14 expansion, the wave-1 COVID truth is weak, and the fatal truth is wave-2. 'B cells unchanged' is not calibrated.",
+    "carrying_draws": {
+     "CD14, severity": "0-4 (wave-1 robust)",
+     "CD14, COVID": "0,1,2,4 (wave-1 calibration uninformative in 4/5)",
+     "CD14, fatal": "0,1,2,4 (1/5 after CD14-fraction adjustment)",
+     "intermediate monocytes": "0-4 in all three contrasts",
+     "cDC2": "severity 4/5, fatal 5/5, COVID none",
+     "B-cell sparing": "none (no per-sample skill)"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv. Paired-test check: CD14 PASS in 15/15 draw x contrast cells (also with TMM); ratio COVID 0.77-1.55, severity 0.54-1.04, fatal 0.47-0.85; Spearman 0.40-0.54. Wave-1 COVID truth -0.16 to -0.30 (p 0.07-0.27) is uninformative in 4/5 draws while generated is -0.27 to -0.44. Fatal truth: 7 of 8 donors wave 2. B HLA-II has no per-sample skill (Spearman -0.14 to 0.17); naive-B COVID gate fails in draws 0 and 4.",
+     "replication": "5/5 same sign; survives age/sex, sex flag, training donors, GEE with days, leave-one-donor-out, wave-1 severity, label confidence and per-cell depth. The generator inflates the CD14 fraction in cases (log2 +0.45 to +1.69 vs observed -0.28 to +0.93); after adjusting for it CD14 holds in 3/5 (COVID), 4/5 (severity) and 1/5 (fatal) draws, while intermediate monocytes and cDC2 stay 5/5.",
+     "observed": "Observed CD14 -0.43 (COVID, p 0.001), -0.58 (severity, p 0.001), -0.53 (fatal, p 0.003); intermediate -0.40 to -0.55. B cells are not unchanged: wave-1 COVID+ naive B HLA-II +0.22 (p 0.0009), memory B +0.15 (p 0.012); fatal naive B -0.23 to -0.45 under three other normalisations.",
+     "beyond_bulk": "Localisation only, of a known effect. Bulk HLA-II falls (-0.52, -0.63, -0.74 log2; p <= 0.011); bulk HLA-II minus monocyte markers tracks observed CD14 HLA-II better (0.71-0.74 vs 0.43-0.48; partial -0.06 to 0.14); none of 45 'adds to bulk' tests survives BH (min q 0.05). csSAM misplaces the drop."
+    },
+    "verdict_reasons": [
+     "Part of the CD14 effect is composition: generated case samples get far more CD14 cells and their HLA-II falls too steeply with abundance.",
+     "B-cell sparing is not calibrated, and observed B HLA-II rises in wave-1 COVID+ and falls in fatal patients.",
+     "The CD14-minus-B interaction holds only under the lens's score; with sum, robust-library or TMM normalisation it is not significant for fatal and mostly for severity (ribosomal-protein shifts move CPM).",
+     "COVID is effectively a wave-1 comparison with a weak wave-1 truth; fatal is nested in severity, enriched for wave 2 and late sampling.",
+     "cDC2 for COVID has no observed support (-0.08, p 0.15; wave 1 +0.04).",
+     "'Progressive loss over the illness' is not supported for severity: the generated severity effect is largest at days 0-7.",
+     "Nothing beyond bulk per patient; the novel part (B sparing) is the uncalibrated part."
+    ],
+    "refuted_subclaims": [
+     "B cells unchanged",
+     "cDC2 involvement for COVID+ vs COVID-",
+     "a novel localisation"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 3C (PDF p.6) and Fig. 4C, steeper HLA class II decline in severe course (PDF p.8-9); Gisby 2022 Fig. 3c bulk HLA-DRA (PDF p.6). mDC HLA-DR loss: Arunachalam 2020 (PDF p.7-8). Stephenson PDF p.9 also reports HLA time x severity effects in B cells.",
+    "next_steps": [
+     "Report severity only, with intermediate monocytes and cDC2 as the robust carriers and a CD14-fraction-adjusted version.",
+     "Add HLA-II programme rows per cell type to the trust layer.",
+     "Retest B-cell sparing with a normalisation-robust score; obtain monocyte/B HLA-DR flow data for bulk-only patients if they exist."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-1_recompute/v02_effects.csv",
+     "verify/IMP-PROG-1_recompute/v02_calib.csv",
+     "verify/IMP-PROG-1_recompute/v05_adjusted.csv",
+     "verify/IMP-PROG-1_recompute/v05_coupling.csv",
+     "verify/IMP-PROG-1_recompute/v07_tracking.csv",
+     "verify/IMP-PROG-1_recompute/v09_verdict_table.csv",
+     "verify/IMP-PROG-1_calib/v01_calib.csv",
+     "verify/IMP-PROG-1_calib/v02_tmm_calib.csv",
+     "verify/IMP-PROG-1_calib/v04_gate_summary.csv",
+     "imp_programs/programs_calib.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-1",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only Imperial patients, the generated cells place the HLA class II loss in CD14 monocytes, intermediate monocytes and cDC2, and leave naive and memory B cells unchanged. This holds for COVID+ vs COVID-, for severe/critical vs mild/moderate and for fatal vs survived. The observed paired cells show the same localisation. The HLA-II drop itself is already visible in the bulk.",
+    "kind": "program",
+    "feature": "HLA-II programme score: mean log2 CPM of HLA-DRA, -DRB1, -DPA1, -DPB1, -DQA1, -DMA, -DMB and CD74 (the genes expressed in the type), per sample x type pseudobulk with at least 10 cells, in CD14 monocytes, intermediate monocytes, cDC2 and naive/memory B. Also a within-sample interaction: score(CD14, intermediate monocyte or cDC2) minus score(naive B) in the same sample, over the 8 genes expressed in both types.",
+    "comparison": "COVID+ vs COVID- (bulk-only 58 vs 19 donors); severe/critical vs mild/moderate among COVID+ (28 vs 30 donors); fatal vs survived among COVID+ (12 vs 46 donors). Donor means; draw analysed one at a time.",
+    "effect_bulkonly": "CD14 HLA-II difference in log2, draws 0-4: COVID -0.33, -0.50, -0.37, -0.15, -0.39 (draw 1 group means 8.72 vs 9.22); severity -0.29, -0.31, -0.34, -0.24, -0.39 (draw 4 8.56 vs 8.95); fatal -0.33, -0.46, -0.39, -0.13, -0.45. Intermediate monocytes -0.21 to -0.42 in every draw and contrast. cDC2: severity -0.20 to -0.41, fatal -0.20 to -0.49, COVID -0.07 to -0.29. Naive and memory B -0.07 to +0.09 (no draw significant). CD14 minus naive B interaction: COVID -0.34, -0.48, -0.43, -0.18, -0.36; severity -0.29, -0.23, -0.32, -0.22, -0.38; fatal -0.26, -0.35, -0.41, -0.09, -0.39. Caveat: in draws 0-2 the generated CD16 monocytes also lose HLA-II with severity (-0.15 to -0.17, p <= 0.02), but observed CD16 monocytes do not (+0.05, p 0.76). Here the generator over-extends the effect.",
+    "stats": "Donor-aware Welch test on donor means (s03, s05, s06). Programme BH q is computed over programme x type within each draw x contrast. CD14 q: COVID 0.006, 0.0007, 0.009, 0.31, 0.011; severity 0.003, 0.015, 0.007, 0.024, 0.0008; fatal 0.022, 0.009, 0.003, 0.46, 0.004. Interaction q is BH over the 15 pre-specified interaction tests per draw. CD14-B q: COVID 0.005, 0.004, 0.033, 0.39, 0.010; severity 0.002, 0.059, 0.008, 0.085, 0.0005; fatal 0.017, 0.059, 0.001, 0.70, 0.009. Intermediate-B severity q: 0.002, 0.025, 0.058, 0.0006, 0.0001. Sensitivity analyses: OLS on donor means with age and sex; wave-1 samples only; without samples whose donor trained the draw; without the sex-flag sample (C137_positive_2); Mann-Whitney. All keep the sign and size (severity wave-1 CD14-B -0.25 to -0.34, p <= 0.015 in 5/5 draws). Using only the first COVID+ sample per donor weakens the fatal effect (p 0.008-0.84) and partly the severity effect. This fits progressive HLA-II loss over the illness.",
+    "draws_same_sign": "5/5 draws for the CD14 and intermediate-monocyte programme and for both interactions, in all three contrasts. Draws carrying the CD14 result (PASS gate and drawwise q < 0.05): COVID 0, 1, 2, 4; severity 0-4; fatal 0, 1, 2, 4. Draw 3 is the weakest. All draws used the same sampling seed, so 5/5 is not five independent replications. Per-draw gates are used as the evidence.",
+    "paired_calibration": "HLA-II is not a feature in calibration.csv. I ran a per-draw calibration on each draw's paired TEST samples, restricted the same way as each contrast (programs_calib.csv). CD14 HLA-II passes the gate in 5/5 draws for all three contrasts. Generated/observed contrast ratio: COVID 0.77-1.55, severity 0.54-1.04, fatal 0.47-0.85. Per-sample Spearman 0.40-0.54. Intermediate monocytes pass 5/5 (ratio 0.33-1.03). cDC2 passes 5/5 for fatal and 4/5 for severity; for COVID it is uninformative (observed test p >= 0.06). With wave-1 (2020) test samples only, severity passes 5/5 for CD14 and intermediate monocytes. CD14-B interaction on the tests: same sign in 15/15 draw x contrast cells; generated/observed ratio 0.62-1.56 (COVID), 0.44-1.00 (severity), 0.38-1.11 (fatal); Spearman 0.22-0.44. Gene-level within-CD14 logFC calibration (calib_types.csv) passes 5/5 in all contrasts (r 0.51-0.64). The calibration.csv composition row l2 CD14 monocyte has ratio 0.73-1.25 and Spearman 0.33-0.48. Transfer check: bulk HLA-II contrasts agree between the paired and bulk-only sets (COVID -0.48 vs -0.52; severity -0.57 vs -0.63; wave-1 severity -0.75 vs -0.67). There is no within-group set shift (|difference| <= 0.05; fatal +0.38, not significant).",
+    "paired_observed_concordance": "All usable paired samples, donor-aware. CD14 HLA-II: -0.43 (COVID, 34 vs 32 donors, p 0.001); -0.58 (severity, 13 vs 21, p 0.001; wave-1 only -0.70, p 0.02); -0.53 (fatal, 8 vs 26, p 0.003). Intermediate monocytes -0.40, -0.50, -0.55 (p <= 0.001). cDC2 -0.08 (p 0.15), -0.24 (p 0.009), -0.31 (p 0.006). Naive B +0.11, -0.05, -0.15 (not significant). CD14-B interaction -0.45 (p 0.002), -0.49 (p 0.007), -0.38 (p 0.044). Across six types, the generated per-type HLA-II effects correlate with the observed ones at r 0.56-0.94. About 95 % of these observed samples are also in the calibration test sets, so honesty items 1 and 3 are not independent.",
+    "beyond_bulk": "Localisation only. The bulk HLA-II score falls (bulk-only -0.52, -0.63, -0.74 log2, p <= 0.011), but the bulk cannot tell which cell type carries the drop. csSAM on the bulk-only bulk with NNLS fractions puts it in the wrong place: for COVID, monocytes +0.35 and B -1.16; for severity, monocytes +2.6. Even with the observed fractions on paired donors, csSAM gives implausible sizes (fatal -8.5). Per patient, the bulk HLA-II score tracks the observed CD14-B interaction better than the generated value (Spearman 0.64-0.72 vs 0.22-0.44; partial Spearman -0.08 to 0.16). For the clinical association, the within-CD14 HLA-II score adds to the bulk HLA-II score only for fatal (likelihood-ratio p 0.028-0.047 in draws 1, 2, 4; the observed score in paired donors also adds, p 0.034) and for severity in draw 4 (p 0.046; observed p 0.025). It does not add for COVID.",
+    "novelty": "Monocyte HLA class II loss with severity is already reported for this cohort's single-cell half: Stephenson 2025, PDF p.6 (Fig. 3C, HLA-DPB1/DPA1/DRB1/DRA/DQA1 lower in total monocytes) and p.9 (Fig. 4C, steeper decline in CD14 monocytes with a severe course). Gisby 2022, PDF p.5 (Fig. 3c), reports lower bulk HLA-DRA with severity. Neither text states cDC2 involvement or that B cells are spared (I searched both). HLA-DR-low monocytes in severe COVID are also reported by Schulte-Schrepping 2020 and COMBAT 2022.",
+    "strength": "strong",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s03_programs.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_draws.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_observed.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/program_map.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05_localisation.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_interaction.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_interaction_bh.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_profiles.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s06_hla_focus.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/hla_sensitivity.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/hla_tracking.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/hla_bulk_baseline.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_hla_mono_b.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_contrast_agreement.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_set_shift.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/calib_types_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/group_means.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/bulk_baseline_programs.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "ad9ebf811ac259ec3",
+     "verdict": "qualified",
+     "recomputed": "I wrote my own code (vc.py, v02-v09) from the compact files: pb_l2/pb_l1, cells.parquet, markers.npz, meta.csv, calibration.csv and the bulk npz. Scores are lcpm2 (edgeR-style log2 CPM, prior 2, mean of the 8 genes, at least 10 cells). Tests use donor means with Welch.\n\nREPRODUCTION (bulk-only, draws 0-4). The claimed numbers reproduce to 2 decimals.\n- CD14 HLA-II:\n  - COVID: -0.33/-0.50/-0.37/-0.15/-0.39 (p .003/.0001/.002/.24/.005).\n  - Severity: -0.29/-0.31/-0.34/-0.24/-0.39 (p ≤ .007).\n  - Fatal: -0.33/-0.46/-0.40/-0.13/-0.45 (p .006/.002/.0003/.34/.0008).\n  - Group means: draw 1 COVID 8.72 vs 9.22; draw 4 severity 8.56 vs 8.95.\n- Intermediate monocytes: -0.21 to -0.42 (all draws and contrasts).\n- cDC2: severity -0.20 to -0.41, fatal -0.20 to -0.50, COVID -0.07 to -0.29.\n- Naive and memory B: -0.11 to +0.09, none with p < .05. Naive B fatal in draw 1 is -0.11, slightly outside the claimed range.\n- CD14 minus naive B interaction:\n  - COVID: -0.34/-0.48/-0.43/-0.18/-0.36.\n  - Severity: -0.29/-0.23/-0.32/-0.22/-0.38.\n  - Fatal: -0.26/-0.35/-0.41/-0.09/-0.39.\n- All of these have the same sign in 5/5 draws.\n- CD16 severity caveat confirmed: -0.15/-0.16/-0.17 (p ≤ .025) in draws 0-2; observed +0.05 (p .76).\n\nPER-DRAW GATES (my recompute on paired test samples; my q is BH over 9 types × 3 contrasts).\n- CD14 passes in 5/5 draws for all three contrasts:\n  - Generated/observed ratio: COVID 0.77-1.55, severity 0.54-1.04, fatal 0.47-0.85.\n  - Spearman 0.40-0.54.\n- Draws that carry CD14 (pass and q < .05): COVID 0, 1, 2, 4; severity 0-4; fatal 0, 1, 2, 4.\n- Intermediate monocytes pass 5/5.\n- cDC2: fatal 5/5; severity 4/5; COVID uninformative in 4/5.\n- CD14 minus naive B passes: COVID 5/5, severity 5/5, fatal 3/5 (2 uninformative).\n\ncalibration.csv composition rows (checked directly):\n- l2 CD14 monocyte: ratio 0.73-1.25, Spearman 0.33-0.48; COVID same sign 5/5, severity 3/5.\n- Naive B: severity same sign 0/5.\n\nOBSERVED PAIRED CELLS (157 samples).\n- CD14: -0.43 (p .001), -0.58 (.001), -0.53 (.003).\n- Intermediate monocytes: -0.40, -0.50, -0.55.\n- cDC2: -0.08 (.15), -0.24 (.009), -0.31 (.006).\n- Naive B: +0.11 (.064), -0.05, -0.15 (.088).\n- CD14 minus naive B: -0.45, -0.49, -0.38 (p .002/.007/.045).\n- Profile correlation over the 6 types: r 0.56-0.94.\n\nBULK.\n- Bulk-only HLA-II score: -0.52 (p < 1e-4), -0.63 (1e-4), -0.74 (.011).\n- Paired vs bulk-only bulk HLA-II within each group: difference ≤ 0.05 (p > .8).\n\nCONFOUNDER CONTROLS ON THE BULK-ONLY EFFECT.\n\nControls that leave sign and size essentially unchanged (CD14 and CD14 minus B):\n- Age + sex.\n- Dropping the sex-flag sample C137_positive_2.\n- Dropping samples whose donor trained the draw.\n- Mann-Whitney test.\n- Sample-level GEE with days since swab (severity CD14 -0.25 to -0.41, p ≤ .003).\n- Leave-one-donor-out (worst p: severity ≤ .013, COVID ≤ .011 except draw 3).\n\nWave-1 only:\n- Severity CD14 5/5 at p ≤ .004.\n- Severity CD14 minus B: -0.25 to -0.34, p ≤ .013.\n- COVID 4/5.\n- Fatal 3/5.\n\nLabel confidence and per-cell checks (markers.npz has HLA-DRA and HLA-DRB1 for every cell):\n- CD14 label confidence does not differ between groups (|Δ| ≤ 0.01).\n- With confidence ≥ 0.9 and per-cell library adjustment, CD14 HLA-DR: COVID -0.31 to -0.51 (5/5 p < .05), severity -0.20 to -0.55 (5/5), fatal 4/5. Draw 0 is null for fatal.\n\nCell number and type fraction:\n- Generated CD14 HLA-II falls as the generated CD14 fraction rises: Spearman -0.31 to -0.62. In observed cells the same coupling is weaker: -0.20 to -0.27.\n- Bulk-only CD14 log2 fraction is much higher in the case groups (+0.45 to +1.31 COVID, +0.87 to +1.35 severity, +1.18 to +1.69 fatal). In observed paired cells it is -0.28, +0.28 and +0.93 (none with p < .05).\n- After adjusting for the type's fraction, observed effects are unchanged (-0.45, -0.49, -0.43).\n- Bulk-only CD14 after fraction adjustment shrinks:\n\n| Contrast | Draws 0-4 (log2) | Draws with p < .05 |\n|---|---|---|\n| COVID | -0.24, -0.35, -0.23, -0.08, -0.16 | 3/5 |\n| Severity | -0.18, -0.23, -0.23, -0.17, -0.25 | 4/5 |\n| Fatal | -0.15, -0.27, -0.24, -0.02, -0.21 | 1/5 |\n\n  - CD14 minus B after fraction adjustment: COVID 3/5, severity 4/5, fatal 0/5.\n- Intermediate monocytes stay at p < .05 in 5/5 draws for all three contrasts (-0.16 to -0.41). cDC2 for severity and fatal: 5/5.\n\nPer-sample tracking on paired test samples (observed CD14 HLA-II):\n- Generated value: 0.43-0.48.\n- Bulk HLA-II score: 0.63-0.68.\n- Crude bulk proxy (HLA-II minus CD14/FCN1/LYZ/VCAN): 0.71-0.74.\n- Partial Spearman of generated given bulk: 0.03-0.21.",
+     "issues": [
+      "The bulk-only CD14 HLA-II drop is partly tied to composition. The generator over-expands CD14 monocytes in the case groups (log2 fraction +0.45 to +1.69 against observed -0.28 to +0.93). It also links lower HLA-II to higher CD14 abundance more strongly than the observed cells do (Spearman -0.31 to -0.62 vs -0.20 to -0.27). After adjusting for the CD14 fraction, the bulk-only CD14 effect shrinks to -0.08 to -0.35 and stays at p < .05 in only 3/5 draws (COVID), 4/5 (severity) and 1/5 (fatal). CD14 minus naive B goes to 3/5, 4/5 and 0/5. Observed effects do not change under the same adjustment. Intermediate monocytes (5/5 in every contrast) and cDC2 (severity and fatal) are the robust carriers of the localisation, so 'all sensitivities keep the sign and size' overstates the CD14 result.",
+      "'B cells unchanged' is not a calibrated inference. Generated naive and memory B HLA-II does not track the observed values per sample (Spearman -0.13 to 0.16 and -0.01 to 0.19). For COVID, the naive B gate fails in draws 0 and 4. The generator shows B ≈ 0 in every contrast regardless of the truth.",
+      "The observed B cells are not unchanged in every contrast:\n  - COVID, wave 1: naive B +0.22 (p .001) and memory B +0.15 (p .012) go up.\n  - Fatal: naive B HLA-DRA/DRB1 -0.25 (p .03) and memory B per-cell -0.42 (p .03) go down; with the 8-gene programme naive B is -0.15 (p .09).\n'The observed cells show the same localisation' holds cleanly only for severity. For COVID and fatal it holds as a CD14-vs-B interaction, not as B sparing.",
+      "The bulk-only COVID contrast is essentially a wave-1 comparison: all 19 COVID- samples and 126 of 143 COVID+ samples are wave 1. On wave-1 paired test samples, the observed CD14 COVID effect is weak (-0.17 to -0.30, p .07-.26). The CD14 gate is uninformative in 4/5 draws, and generated/observed is 1.2-2.1, so the generator probably overstates the COVID CD14 loss. In all observed wave-1 cells, CD14 is -0.29 (p .069) and intermediate monocytes -0.22 (p .035).",
+      "The fatal contrast is not separable from severity, wave and timing:\n  - All 12 fatal donors are severe/critical.\n  - 5/12 fatal donors are wave 2 (dexamethasone era) vs 5/46 survivors.\nRestricted analyses, by draws with p < .05 for CD14 (CD14 minus B where given):\n  - Fatal within severe/critical: 3/5 (CD14 minus B 1/5).\n  - Wave 1 only: 3/5.\n  - First COVID+ sample per donor: 3/5.\n  - Fraction-adjusted: 1/5.\nThe fatal effect is late-illness: on the first COVID+ sample per donor, CD14 minus B is p .46/.28/.008/.86/.065.",
+      "The severity effect is concentrated in early samples. At day 14 or earlier, CD14 holds at p < .05 in 5/5 draws. From day 15, it is -0.16 to -0.30 with no draw at p < .05. That does not fit the claim's 'progressive HLA-II loss over the illness' for severity; it fits fatal only.",
+      "Draw 0 passes the 8-gene gate but does not reproduce the HLA-DR genes themselves for severity or fatal on its test samples (HLA-DRA+DRB1: generated -0.08/-0.09 vs observed -0.41/-0.59). Draw 3 does not carry COVID or fatal.",
+      "Per sample, the generated value adds almost nothing beyond the bulk: partial Spearman given the bulk HLA-II score is 0.03-0.21. A crude bulk proxy (HLA-II minus CD14/FCN1/LYZ/VCAN) tracks observed CD14 HLA-II better (0.71-0.74 vs 0.43-0.48). The only beyond-bulk content is group-level localisation, and it agrees with prior knowledge (monocyte HLA-DR loss in severe COVID).",
+      "B cell at L1 (all B subsets) shows significant HLA-II drops in draws 1 and 3 (for example draw 1: COVID -0.17, p .002; fatal -0.34, p .004). The 'B unchanged' statement holds only for naive and memory B at L2. With age and sex adjustment, naive B reaches p < .05 in a few draw × contrast cells (-0.06 to -0.13).",
+      "All draws share sampling seed 20260927, so 5/5 sign agreement is not five independent replications (the claim already says this). Per-draw gates are the evidence."
+     ],
+     "reasoning": "The effect reproduces exactly from my own code: CD14, intermediate-monocyte, cDC2, naive/memory B and interaction effects, group means, p-values, the per-draw paired-test gates and observed concordance all match the claim.\n\nThe core monocyte HLA-II loss survives the basic controls listed in the recomputed section. These are age and sex, the sex-flag sample, training-donor samples, GEE with days since swab, leave-one-donor-out, wave-1 restriction for severity, label-confidence filtering and per-cell library adjustment. It is not refuted.\n\nThree things keep it at qualified:\n\n1. Composition. Part of the CD14 effect comes from a generator artefact: case groups get far more generated CD14 monocytes than observed, and generated HLA-II per cell falls too steeply with that abundance. Adjusting for the fraction weakens CD14, and especially CD14 for fatal. Intermediate monocytes and cDC2 stay robust, so the localisation itself stands, carried mostly by those types.\n\n2. B-cell sparing is not calibrated. The generator's B-cell HLA-II does not track the truth per sample. The observed B cells rise in COVID wave 1 and fall in fatal patients, so 'B unchanged, same in observed' holds cleanly only for severity.\n\n3. Two contrasts are confounded:\n   - COVID is in effect a wave-1 comparison, and the matching wave-1 calibration is weak, with the generator overestimating.\n   - Fatal is nested in severity, enriched for wave 2 (dexamethasone era) and late sampling.\n\nAs the claim admits, per-sample information beyond the bulk is nil.\n\nBest-supported statement: in the severity contrast (draws 0-4, wave-1 robust), generated cells place the HLA-II loss in CD14 and intermediate monocytes and cDC2 rather than in naive B, consistent with the observed cells. COVID and fatal are weaker, and the CD14 magnitude is partly composition-driven.\n\nAll files were written only in my verify dir; nothing was published to the paper archive from this subagent.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/vc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v01_calibration_csv_rows.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v01_calibration_csv_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_recompute.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_interactions.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_calib_interactions.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v02_summary.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v03_confounders.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v03_confounders.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v03_cells_lib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v04_cells_lib_observed.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v04_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v05_fraction_coupling.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v05_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v05_coupling.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v05_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v06_percell_confidence.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v06_percell.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v07_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v07_bulk_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v07_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v07_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v07_bulkonly_link.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v08_profiles.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v08_profiles.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v09_verdict_table.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v09_verdict_table.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_recompute/v09_verdict_summary.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a653c12e79a67ef93",
+     "verdict": "qualified",
+     "recomputed": "I re-implemented the analysis independently: my own scripts, which do not import ip_common. Unless noted, numbers use the claim's own score: the mean log2 CPM (edgeR-style prior of 2) of the 8 genes, from pseudobulks with at least 10 cells, compared on donor means with a Welch test.\n\nBULK-ONLY, draws 0 to 4 (exact reproduction of the candidate)\n- CD14 HLA-II, COVID: -0.33 (p 0.003), -0.50 (8e-5), -0.37 (0.002), -0.15 (0.24), -0.39 (0.005). Draw-1 group means are 8.72 vs 9.22.\n- CD14 HLA-II, severity: -0.29, -0.31, -0.34, -0.24, -0.39 (p 1e-4 to 0.007).\n- CD14 HLA-II, fatal: -0.33, -0.46, -0.39, -0.13 (p 0.34), -0.45.\n- CD14 minus naive-B interaction: COVID -0.34, -0.48, -0.43, -0.18, -0.36. Severity -0.29, -0.23, -0.32, -0.22, -0.38. Fatal -0.26, -0.35, -0.41, -0.09, -0.39.\n- Naive B ranges from -0.11 (fatal, draw 1, p 0.17) to +0.02; memory B from -0.06 to +0.09.\n- The candidate's BH q-values match their own p-values (largest difference 1e-5).\n\nPAIRED-TEST CALIBRATION FOR CD14 HLA-II (my computation; HLA-II is not in calibration.csv)\nFormat is generated/observed difference and ratio.\n- COVID: -0.43/-0.42 (1.00), -0.47/-0.33 (1.44), -0.49/-0.31 (1.55), -0.30/-0.39 (0.77), -0.37/-0.39 (0.96). Pooled -0.42/-0.42.\n- Severity ratios 0.70, 1.05, 0.74, 0.54, 0.91. Pooled -0.45/-0.58.\n- Fatal ratios 0.67, 0.85, 0.82, 0.47, 0.82. Pooled -0.39/-0.53.\n- Per-sample Spearman is 0.40 to 0.54.\n- Gate: PASS in 15/15 draw x contrast cells (14/15 under a stricter gate). TMM normalisation also gives PASS 15/15.\n- Intermediate monocytes: PASS 15/15 under all four normalisations.\n- cDC2: PASS 5/5 for fatal and 4/5 for severity. For COVID it is uninformative in 4/5 (main score).\n\nRESTRICTED CALIBRATIONS\n- Wave-1 COVID (the bulk-only COVID side is 48 of 58 donors from wave 1): CD14 is UNINFORMATIVE in 4/5 draws. Observed effect is -0.16 to -0.30 (p 0.07 to 0.27); generated is -0.27 to -0.44.\n- Wave-2 fatal: CD14 is UNINFORMATIVE in 4/5 draws.\n- Naive B: generated vs observed per-sample Spearman is -0.14 to 0.17. COVID gate FAILs in draws 0 and 4. In wave-1 COVID, observed naive B rises +0.16 to +0.24 (p 0.0008 to 0.031) while generated is -0.03 to +0.12: FAIL in draws 0 and 4, SIGN-ONLY in draws 1 to 3.\n\nOBSERVED, ALL PAIRED SAMPLES\n- Main score: CD14 -0.42 (p 7e-4), -0.58 (8e-4), -0.53 (0.003). Naive B +0.11 (0.064), -0.05, -0.15 (0.088). CD14 minus B -0.45 (0.002), -0.49 (0.007), -0.38 (0.045).\n- Wave-1 COVID: CD14 -0.29 (p 0.069); naive B +0.22 (p 0.0009); memory B +0.15 (p 0.012); cDC2 +0.04.\n- Other normalisations, fatal: naive B -0.23 (sum, p 0.003), -0.33 (robust library, p 0.0007), -0.45 (TMM, p 0.004); memory B -0.38 (TMM, p 0.003). The fatal CD14 minus B interaction is -0.22 (p 0.13), -0.20 (0.16), -0.19 (0.26).\n- Other normalisations, severity: the interaction is -0.21 (p 0.14 and 0.13, sum and robust) and -0.38 (p 0.037, TMM).\n- The CD14 loss itself holds under every normalisation: severity -0.34 to -0.61; fatal -0.44 to -0.67; COVID -0.19 to -0.48.\n\nAGAINST THE BULK\n- Bulk-only bulk HLA-II score: -0.52 (p 2e-5), -0.63 (7e-5), -0.74 (0.011). Bulk HLA-II minus monocyte markers: -0.68, -0.69, -0.90.\n- Bulk-only fatal: the B-marker score falls -0.80 (p 0.038), and bulk HLA-II minus B markers is +0.06. In the paired set the bulk fatal contrast is only -0.34 (p 0.12).\n- Per patient, against observed CD14 HLA-II: Spearman is 0.43 to 0.48 for the generated cells, 0.63 to 0.68 for bulk HLA-II, and 0.71 to 0.74 for bulk HLA-II minus monocyte markers. The generated score adds little: partial Spearman 0.03 to 0.21 given bulk HLA-II, and -0.06 to 0.14 given the bulk ratio.\n- Slope on bulk HLA-II: CD14 0.41 to 0.58 generated vs 0.59 to 0.62 observed; naive B 0.05 to 0.09 generated vs about 0 observed.\n- None of the candidate's 45 \"adds to bulk\" likelihood-ratio tests survives BH (minimum q 0.05).\n\nTIME AND WAVE\n- With donor-clustered OLS adjusting for days and wave 2, CD14 severity is still -0.22 to -0.34 (p < 0.01 in 5/5 draws).\n- Generated severity effect by window: days 0 to 7 -0.28 to -0.56; days 15 to 60 -0.16 to -0.30 (not significant).\n- In severe patients the generated CD14 HLA-II slope is +0.12 to +0.26 per 14 days in every draw.",
+     "issues": [
+      "Only the monocyte (CD14, intermediate) and cDC2 parts are supported. The bulk-only CD14 and intermediate-monocyte HLA-II losses hold in calibration, in the observed paired cells and in the literature, and they survive every normalisation, time and wave adjustment, dropping training donors, and dropping the donors with paired single cells. Draws carrying the CD14 result are as the candidate states: COVID 0,1,2,4; severity 0-4; fatal 0,1,2,4. Draw 3 is the weakest.",
+      "'B cells unchanged' is not calibrated. The generator has no per-sample skill for B-cell HLA-II (generated vs observed Spearman -0.14 to 0.17). Naive-B COVID calibration FAILs in draws 0 and 4. So the flat generated B cells reflect an insensitive generator, not a measurement.",
+      "In wave 1 the observed B cells go the other way. The bulk-only COVID side is mostly wave 1 (48 of 58 donors). There, observed naive B HLA-II rises +0.22 (p 0.0009) and memory B +0.15 (p 0.012). The rise holds under all four normalisations (+0.17 to +0.23, p <= 0.035). The generator does not reproduce it: wave-1 naive-B gates are FAIL in draws 0 and 4 and SIGN-ONLY in draws 1-3.",
+      "The observed B-cell sparing in fatal depends on the score. With the HLA-II share of the library (sum), a library excluding HLA-II and the 50 most abundant genes (robust), or TMM, observed fatal naive B falls -0.23, -0.33 and -0.45 (p 0.0007-0.004) and memory B -0.20 to -0.38. The fatal CD14 minus naive-B interaction is then not significant (p 0.13-0.26). Severity is similar under sum and robust (p 0.13-0.14) and holds only under TMM (p 0.037). A likely driver is ribosomal-protein genes: they are 56-59 % of naive-B UMIs and fall in disease (e.g. -0.033 of the library in observed fatal naive B), which shifts CPM.",
+      "The CD14 minus B interaction gates PASS only with the claim's own score: under sum, robust and TMM the observed interaction disappears and the gates turn UNINFORMATIVE for fatal (5/5) and mostly for severity.",
+      "The COVID contrast is probably overstated. The wave-1 CD14 COVID calibration is UNINFORMATIVE in 4/5 draws. The observed wave-1 CD14 effect is -0.29 (p 0.069) on the claim's score and about -0.05 under sum and robust, while the generated bulk-only wave-1 effect is -0.30 to -0.44. cDC2 for COVID has no observed support: -0.08 (p 0.15) overall and +0.04 in wave 1; only TMM gives -0.34. So 'cDC2 for COVID+ vs COVID-' should be dropped.",
+      "The fatal calibration population does not match the bulk-only one. Paired fatal truth is 7 of 8 donors from wave 2 (the dexamethasone era; Stephenson 2025 PDF p.11 reports dexamethasone-associated monocytes with lower HLA-DRA/DRB5, in the 2021 cohort only). Bulk-only fatal donors are 7 wave-1 and 5 wave-2. The bulk HLA-II fatal contrast is -0.34 (p 0.12) in the paired set vs -0.74 (p 0.011) in the bulk-only set.",
+      "The 'progressive loss' reading is not supported for severity. In the generated data the severity effect is largest at days 0-7 (-0.28 to -0.56) and not significant after day 15. In severe patients the generated CD14 HLA-II slope is positive in every draw (+0.12 to +0.26 per 14 days).",
+      "Little is added beyond the bulk. Per patient, bulk HLA-II minus monocyte markers predicts observed CD14 HLA-II better (Spearman 0.71-0.74) than the generated cells do (0.43-0.48); the generated score's partial Spearman is -0.06 to 0.14. None of the candidate's 45 'adds to bulk' likelihood-ratio tests survives BH (minimum q 0.05). What remains is localisation: in bulk-only fatal, the bulk HLA-II drop matches the bulk B-marker drop (bulk HLA-II minus B markers +0.06). Of that localisation, the monocyte part is already known and the B part is not calibrated.",
+      "Novelty is overstated. Stephenson 2025 PDF p.9 reports HLA-driven time-by-severity interactions in both CD14 monocytes and B cells, which cuts against 'B cells spared'. HLA-DR loss on mDCs (cDC2-like) in severe COVID is already reported by Arunachalam 2020 (PDF p.7-8, Fig. 5A-B, a PDF in the paired-cohorts folder). Monocyte HLA class II loss in severe and fatal COVID is reported by Wilk 2021 (PDF p.6), COMBAT 2022 (PDF p.17-18) and Schulte-Schrepping 2020 (PDF p.9, p.11). One citation is off by a page: Gisby 2022's Fig. 3c HLA-DRA panel is on PDF p.6 (the text citing it is on p.5).",
+      "Minor: bulk-only naive-B HLA-II reaches -0.11 (fatal, draw 1), not -0.07 as stated. All draws used the same sampling seed, so agreement across draws is not independent replication; the per-draw gates above are the evidence.",
+      "Housekeeping: I did not start any GPU work and made no archive changes (no archive delta from this subagent). v01_lmm_SUPERSEDED_failed_mixedlm_fits.csv holds failed statsmodels MixedLM fits and is not quoted; v02_time.csv (donor-clustered OLS) replaces it."
+     ],
+     "reasoning": "This is an adversarial check of IMP-PROG-1. I re-implemented the analysis independently (v01 to v04), following DATA_GUIDE: every contrast within each draw, each draw gated on its own paired tests, donor-aware tests, CPU only under a 6 GB memory cap. I reproduce every bulk-only, observed and paired-test number the candidate gives. I then attacked the claim on the normalisation, the matching of restrictions, the transfer from paired to bulk-only samples, time and wave, and novelty.\n\n(1) Calibration. HLA-II is not in calibration.csv. Its composition rows for these cell types only describe cell counts and say nothing about HLA-II, so I computed the equivalent paired check per draw and pooled.\n- CD14 and intermediate-monocyte HLA-II loss passes in every draw for all three contrasts, with the right direction, ratio 0.47 to 1.55 and Spearman 0.40 to 0.62. This holds under TMM as well. cDC2 passes for severity and fatal.\n- It fails in the right places, though. For wave-1 COVID, which is the population the bulk-only COVID side mostly comes from, the gate is uninformative.\n- For B cells there is no calibration at all: the generator has no per-sample skill.\n- Fatal calibration rests on wave-2 (dexamethasone-era) donors.\n\n(2) Observed support. The observed paired cells confirm CD14 and intermediate-monocyte HLA-II loss under every normalisation. They do not robustly confirm \"B cells unchanged\".\n- In wave-1 COVID+, observed B-cell HLA-II rises significantly.\n- In fatal, B cells lose HLA-II significantly under three of the four scores, and the CD14 minus B localisation is then not significant.\n- The localisation the candidate reports is therefore an artefact-prone property of its chosen score (mean log CPM over the whole library, with large ribosomal shifts).\n\n(3) Biology and novelty.\n- Monocyte HLA class II loss with severity is already reported in this cohort: Stephenson 2025 PDF p.6 (Fig. 3C) and p.9 (Fig. 4C), and in bulk by Gisby 2022 (PDF p.5-6, Fig. 3c).\n- Stephenson 2025 p.9 also places HLA-driven time-by-severity effects in B cells.\n- mDC/cDC2 HLA-DR loss in severe COVID is known: Arunachalam 2020 PDF p.7-8.\n- Fatal and severe monocyte HLA-II loss is known: Wilk 2021 p.6; COMBAT 2022 p.17-18; Schulte-Schrepping 2020.\n- Glucocorticoids lower monocyte HLA-DR: Stephenson 2025 p.11.\nSo nothing in the claim is new except the B-cell sparing, and that part is unsupported.\n\n(4) Beyond bulk. The bulk HLA-II score already shows the drop. A bulk monocyte-normalised ratio tracks per-patient observed CD14 HLA-II better than the generated cells do. The clinical \"adds to bulk\" tests do not survive multiplicity correction.\n\nVerdict: qualified. The supportable statement is narrower: \"Generated cells place the bulk HLA-II loss in CD14 and intermediate monocytes, and in cDC2 for severity and fatal, consistent with paired calibration (CD14: draws 0-4 for severity, 0,1,2,4 for COVID and fatal) and with the observed paired cells; this reproduces known biology.\" The parts that do not hold are \"B cells unchanged\", cDC2 for COVID, a novel localisation, and information beyond the bulk.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_hla_verify.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_hla_verify.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_bulk_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v01_lmm_SUPERSEDED_failed_mixedlm_fits.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_norm_time.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_norm_time.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_tmm_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_tmm_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_tmm_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_gene_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_ribo.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v02_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v03_time_severity.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v03_time_severity.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v03_time_severity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v04_quoted_tables.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v04_quoted_tables.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/v04_gate_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/Arunachalam2020_Science_systems_immunity_mild_vs_severe_COVID19.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/Wilk2021_Innate_immunity_hematopoiesis_dysregulation_COVID19.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/COMBAT2022_Blood_atlas_COVID19_severity.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-1_calib/pdftext/SchulteSchrepping2020_Severe_COVID19_dysregulated_myeloid.txt"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-PROG-3",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 5,
+    "theme": "monocyte alarmin / MS1 / CD163",
+    "synthesis_strength": "moderate for CD14 monocytes (draws 0-4) with inflated size; not supported for CD16, several listed genes, or fatal beyond severity",
+    "reportable_statement": "Generated CD14 monocytes of bulk-only severe/critical (and COVID+, fatal) patients express more of the Stephenson 2025 severity programme (CD163, MCEMP1, PLAC8, S100A8/9/12) and of MS1/S100 genes than mild/moderate patients in all five draws (Stephenson set +0.39 to +0.55 log2), calibrated 5/5 in CD14 (wave-1 severity 5/5). It is a gene-level shift: the discrete MS1 state is under-generated (0.04-0.37x). Holding the generated CD14 count fixed roughly halves it.",
+    "carrying_draws": {
+     "CD14 Stephenson set / MS1 / S100": "0-4 in COVID, severity and fatal (draw 2's MS1/S100 severity effect is explained by the CD14 count)",
+     "CD16 S100 severity": "none after identity adjustment",
+     "MS1 in CD16, fatal": "2,4 (lens table; ratio 0.18-0.44)",
+     "MS1 in intermediate monocytes, severity": "none (log2 CPM+1)",
+     "fatal beyond severity": "none"
+    },
+    "honesty": {
+     "calibration": "Programme not in calibration.csv; paired-test CD14 PASS 5/5 in all three contrasts (severity ratio 0.56-0.82; Spearman 0.36-0.77); wave-1 severity 5/5. calibration.csv 'MS1 monocyte (IL1R2+)' state: ratio 0.04-0.37 and contrast 0.04-0.36x (COVID+-only severity FAIL in 4/5 draws, synthesis i01). 'l2 | CD14 monocyte' severity sign wrong in draws 1-2. With the CD14 count held fixed the paired ratio drops to 0.29-0.64x.",
+     "replication": "5/5 draws in all 27 programme x cell type x contrast cells; survives mixed model, GEE, one sample per donor, age/sex, wave 1, training donors, sex flag, days, label confidence and MS1-state exclusion. Generated CD14 count and score are coupled (Spearman 0.43-0.78 vs observed 0.02-0.35): count-adjusted CD14 severity is 0.12-0.34 (Stephenson set), 0.01-0.47 (MS1), 0.09-0.21 (S100), about 0 in draw 2.",
+     "observed": "Observed CD14 Stephenson set +0.90 (severity, p < 1e-4), +0.78 (fatal), +0.64 (COVID); MS1 +1.06 to +1.20; S100 +0.59. Fatal within severe/critical +0.11 (p 0.72; 8 vs 5 donors). ALOX5AP, CYP1B1 and RETN do not rise in observed CD14. The observed CD16 rise tracks classical identity genes (CD14 +1.05, VCAN +1.15) and disappears after identity adjustment.",
+     "beyond_bulk": "Little. Bulk Stephenson score +0.76 (severity) to +1.07 (fatal), p < 0.001; the bulk tracks observed CD14 better (partial -0.14 to 0.30). The S100 'adds to bulk' association (draws 0, 3, 4) fails on each draw's own paired test donors (LR p 0.38, 0.46, 0.15)."
+    },
+    "verdict_reasons": [
+     "CD14 effect sizes are inflated by the generator's spurious CD14 expansion.",
+     "'All five draws carry it in CD14, CD16 and intermediate monocytes' is wrong: MS1 in CD16 fatal only draws 2, 4; intermediate MS1 severity 0/5 under log2(CPM+1); CD16 S100 severity 0-2/5.",
+     "CD16 rises are classical-identity contamination in observed cells; generated CD16 S100 still rises (spurious).",
+     "Three listed MS1 genes (ALOX5AP, CYP1B1, RETN) are not in the observed shift; IL1R2 is under-generated (0.1-0.45x).",
+     "Fatal is the severity contrast again and its calibration does not transfer (bulk-only fatal programme scores 0.47-0.60 log2 above paired fatal).",
+     "TNF loss, Stephenson's top monocyte severity gene, is not reproduced (0/5)."
+    ],
+    "refuted_subclaims": [
+     "carried by all draws in all three monocyte subsets",
+     "the full MS1 gene list",
+     "S100 adds to the bulk",
+     "fatal as a separate contrast"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 PDF p.6 (CD163, S100A8/A9/A12, MCEMP1 up; PLAC8 in CD14 monocytes; TNF down); Gisby 2022 PDF p.5 (module tB, attributed to LDGs); Schulte-Schrepping 2020 p.11; Silvin 2020 p.9; Reyes 2021 p.3; COMBAT 2022 p.8.",
+    "next_steps": [
+     "Report CD14 only, with a CD14-count-adjusted size.",
+     "Use the observed-supported genes (S100A8/9/12, MCEMP1, PLAC8, CD163, IL1R2, CLU).",
+     "Fix IL1R2/MS1-state under-generation; condition on or remove the bulk LDG/alarmin signal before attributing it to monocytes."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-3_recompute/v08_count_adjusted.csv",
+     "verify/IMP-PROG-3_recompute/v07_coupling.csv",
+     "verify/IMP-PROG-3_recompute/v08_genes.csv",
+     "verify/IMP-PROG-3_recompute/v09_fatal_within_severe.csv",
+     "verify/IMP-PROG-3_recompute/v10_carry_counts.csv",
+     "verify/IMP-PROG-3_calib/c01b_carry.csv",
+     "verify/IMP-PROG-3_calib/c02_genes_summary.csv",
+     "verify/IMP-PROG-3_calib/c02_adjusted.csv",
+     "verify/IMP-PROG-3_calib/c04_beyond_bulk.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-3",
+    "cohort": "imperial_eskd",
+    "claim": "In all three monocyte subsets of bulk-only patients, the generated cells raise the alarmin/MS1/CD163 programme with COVID, with a severe/critical course and with fatal outcome, as observed paired monocytes do. The programme includes S100A8/A9/A12, MCEMP1, PLAC8, CD163 and the MS1 genes IL1R2, RETN, ALOX5AP, CYP1B1 and CLU. It is a gene-level shift inside monocytes: the discrete MS1 state is under-generated. The generator also adds a spurious CD14-monocyte expansion in severe patients.",
+    "kind": "program",
+    "feature": "Stephenson-2025 severity-up monocyte programme (CD163, MCEMP1, PLAC8, S100A8, S100A9, S100A12), the MS1 programme and S100 alarmins, scored in CD14, CD16 and intermediate monocytes.",
+    "comparison": "COVID+ vs COVID- (58 vs 19 donors); severe/critical vs mild/moderate among COVID+ (28 vs 30); fatal vs survived among COVID+ (12 vs 46). Donor means; draw analysed one at a time.",
+    "effect_bulkonly": "Stephenson set in CD14 monocytes, log2, draws 0-4: severity +0.39, +0.42, +0.37, +0.52, +0.55 (draw 0 group means 11.21 vs 10.82); fatal +0.56, +0.67, +0.56, +0.70, +0.64; COVID +0.48, +0.57, +0.50, +0.71, +0.57. For severity: CD16 +0.65 to +0.83, intermediate +0.45 to +0.61. MS1 in CD14, severity: +0.45, +0.46, +0.41, +0.64, +0.74. S100 in CD14, severity: +0.30, +0.30, +0.21, +0.29, +0.39.",
+    "stats": "Donor-aware Welch test. Drawwise BH q, CD14, severity: Stephenson set 0.001, 0.004, 0.011, 0.0015, 0.0001; MS1 0.001, 0.004, 0.020, 0.002, < 0.001; S100 0.0003, 0.001, 0.007, 0.0002, < 0.001. Results hold with age and sex, with wave-1 samples only (severity Stephenson set +0.30 to +0.51, p <= 0.03), without trained-donor samples and without the sex-flag sample.",
+    "draws_same_sign": "5/5 draws in every monocyte subset and every contrast. All five draws carry (PASS gate and q < 0.05) the Stephenson set and MS1 in CD14, CD16 and intermediate monocytes, and S100 in CD14 and intermediate monocytes, in all three contrasts. S100 in CD16 for severity is carried by draws 1-2 only.",
+    "paired_calibration": "Per-draw programme calibration (programs_calib.csv), CD14: PASS in 5/5 draws for all contrasts. Generated/observed ratio for severity: 0.71-0.82 (Stephenson set), 0.66-0.80 (MS1), 0.56-0.81 (S100). Per-sample Spearman 0.36-0.77, against 0.49-0.80 for the bulk score. Wave-1-only gates: PASS 5/5. calibration.csv state row 'MS1 monocyte (IL1R2+)': level ratio 0.04-0.37, Spearman 0.35-0.56, sign kept in 5/5 draws for COVID and severity. But the contrast is only 0.04-0.36x of observed (severity: generated +0.002 to +0.013 vs observed +0.035 to +0.066). Composition row 'l2 | CD14 monocyte' for severity: generated +0.022 to +0.060 vs observed -0.010 to +0.008, with the wrong sign in draws 1-2.",
+    "paired_observed_concordance": "Observed CD14 monocytes (all paired samples, donor-aware): Stephenson set severity +0.90 (13 vs 21 donors, p < 0.001), fatal +0.78 (p < 0.001), COVID +0.64 (p < 0.001). MS1 severity +1.06 (p < 0.001). S100 severity +0.59 (p 0.001). For severity, the Stephenson set is +1.10 in CD16 and +0.71 in intermediate monocytes.",
+    "beyond_bulk": "Little. The bulk already shows the change: the bulk-only bulk Stephenson-set score is +0.82 (COVID), +0.76 (severity) and +1.07 (fatal), all p < 0.001. A reference atlas would also put these genes in monocytes. Per patient, the bulk score tracks observed CD14 values about as well (partial Spearman -0.14 to 0.37). One association does add: the within-CD14 S100 score adds to the bulk S100 score for severity in draws 0, 3 and 4 (likelihood-ratio p 0.004-0.023), and the observed within-CD14 score does too (p 0.011). The attribution is wrong, though: in observed cells 15 of 17 severity bulk-up monocyte genes are expression-driven, but in generated cells only 1-4 of 14-15 are. The generated cells attribute the rest to composition because of the spurious CD14 expansion.",
+    "novelty": "Reported. Stephenson 2025, PDF p.6: CD163, S100A8/A9/A12 and MCEMP1 rise in monocytes with severity, and PLAC8 rises in CD14 monocytes. Gisby 2022, PDF p.5: S100A9/A12 are in the severity-rising module tB. Also Schulte-Schrepping 2020 and Silvin 2020.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s03_programs.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_draws.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/program_map.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s14_bulk_baseline_programs.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/bulk_baseline_programs.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s13_driver_check.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/driver_check_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/driver_check_composition_rows.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_contrast_agreement.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/group_means.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/fatal_within_severe.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a2c205a720acc707e",
+     "verdict": "qualified",
+     "recomputed": "I wrote my own code from the raw pb_l2.npz, markers.npz, subsample.npz and meta files. Score: mean log2(CPM+1) over the programme genes in each sample x type pseudobulk. Library = the 5,927 measured panel genes; a pseudobulk needs at least 10 cells. Tests use donor means and Welch. A cell-level recomputation matches the pseudobulk scores to within 2e-15.\n\nBULK-ONLY EFFECTS (per draw 0..4)\n- CD14 monocytes, severity (28 vs 30 donors):\n  - Stephenson set: +0.43, +0.48, +0.43, +0.60, +0.73. BH q (27 tests per draw) 0.0002 to 0.004. Draw 0 means 11.15 vs 10.72.\n  - MS1: +0.63, +0.80, +0.61, +0.84, +1.26.\n  - S100: +0.30, +0.30, +0.21, +0.29, +0.39, identical to the claim.\n- Fatal (12 vs 46), Stephenson set: +0.61, +0.76, +0.64, +0.79, +0.81.\n- COVID (58 vs 19), Stephenson set: +0.51, +0.62, +0.66, +0.79, +0.70.\n- Severity, Stephenson set: CD16 +0.97, +0.78, +1.20, +0.90, +0.94; intermediate +0.72, +0.73, +0.68, +0.88, +0.88.\n- All 27 cells (3 programmes x 3 monocyte types x 3 contrasts) are positive in 5/5 draws. An aggregate-CPM score reproduces this too.\n\nROBUSTNESS (CD14, severity, Stephenson set)\n- Random-intercept mixed model: 0.40 to 0.70 (5/5 draws p < 0.05).\n- GEE: 0.40 to 0.70.\n- One sample per donor: 0.41 to 0.76.\n- Age + sex: 0.39 to 0.72.\n- Wave 1 only: 0.34 to 0.67 (p <= 0.017).\n- Without samples of trained donors: 0.40 to 0.75.\n- Without the sex-flag sample: 0.43 to 0.73.\n- Adjusted for days since positive swab (severe patients were sampled later, 13.4 vs 9.3 days, p 0.01): 0.50 to 0.82.\n- Cells with label confidence >= 0.99, without granulocyte-like cells, or without discrete MS1-state cells: unchanged.\n- Shift pattern: the share of CD14 cells with any MS1-gene count rises 7 to 18 points, while MS1-state cells rise only 0.1 to 2 points. This supports a diffuse gene-level shift.\n\nCELL NUMBER (CD14 count)\n- Bulk-only severe patients get +101 to +193 more CD14 cells per 1,500 cells (p <= 0.006 in 5/5 draws).\n- calibration.csv 'l2 | CD14 monocyte', severity: generated +0.022 to +0.060 vs observed -0.010 to +0.008, wrong sign in draws 1-2. The expansion is spurious.\n- Within-group Spearman between log CD14 count and the programme score: generated 0.43 to 0.78, observed 0.02 to 0.35.\n- Count-adjusted bulk-only CD14 severity effects:\n  - Stephenson set 0.12 to 0.34 (4/5 draws p < 0.05).\n  - MS1 0.01 to 0.47 (3/5).\n  - S100 0.09 to 0.21 (4/5).\n  - Draw 2 is about 0 for MS1 and S100.\n- On the paired test samples, the generated/observed ratio for severity falls once the count is held fixed:\n  - Stephenson set: 0.85-1.05 unadjusted → 0.51-0.64 adjusted.\n  - MS1: 0.87-1.38 → 0.43-0.59.\n  - S100: 0.56-0.81 → 0.29-0.49.\n  - Fatal: similar, 0.29 to 0.63 adjusted.\n\nCALIBRATION (my gates)\n- CD14, all three programmes: PASS 5/5 in all three contrasts.\n- Wave-1-only gates:\n  - Severity: PASS 5/5 for all three programmes.\n  - COVID: Stephenson set 5/5, MS1 1/5, S100 0/5 (uninformative).\n  - Fatal: cannot be calibrated (only 1 fatal paired donor in wave 1).\n- Carried (PASS and q < 0.05):\n  - MS1 in CD16: COVID 2/5 (draws 0, 4); fatal 4/5 with generated/observed ratio only 0.18 to 0.44; severity 4/5.\n  - MS1 in intermediate monocytes: COVID 3/5; severity 0/5 (observed test p 0.18 to 0.45; all paired +0.39, p 0.21).\n  - S100 in CD16: severity 1/5 (draw 1 only); COVID 3/5.\n  - Stephenson set in intermediate monocytes: COVID 4/5.\n\nOBSERVED PAIRED CELLS\n- CD14, Stephenson set: severity +0.91 (13 vs 21 donors, p < 1e-4), fatal +0.79, COVID +0.65.\n- CD14 MS1 severity +1.20; CD14 S100 severity +0.59.\n- Severity, Stephenson set: CD16 +1.12, intermediate +0.65.\n- Wave-1 CD16, COVID: MS1 +0.03 (p 0.93), S100 -0.03 (p 0.92), Stephenson set +0.34 (p 0.33).\n- Fatal vs survived within severe patients, CD14:\n  - Observed (8 vs 5 donors): Stephenson set +0.11 (p 0.72), S100 -0.08.\n  - Generated: Stephenson set +0.46 to +0.62 (p <= 0.005).\n- Observed CD14 severity, per gene:\n  - Rise with p < 0.01: S100A8 +0.61, S100A9 +0.52, S100A12 +0.63, MCEMP1 +0.81, PLAC8 +1.77, CD163 +1.10, IL1R2 +2.35, CLU +2.93.\n  - Not significant: RETN +0.44 (p 0.19), CYP1B1 +0.22 (p 0.50), ALOX5AP -0.03 (p 0.95).\n  - Generated draw 2 gives CYP1B1 -0.76.\n\nBULK ALONE\n- Bulk-only bulk Stephenson-set score: +0.82 (COVID), +0.76 (severity), +1.07 (fatal), all p < 1e-4; wave 1 only: +0.76, +0.70, +1.09.\n- Does the within-CD14 score add to the bulk score (likelihood-ratio test)? S100 for severity adds in draws 0, 3, 4 (p 0.015, 0.023, 0.004) and observed adds too (p 0.011). This reproduces the claim.\n- Partial Spearman of generated vs observed CD14 score given the bulk score: -0.14 to 0.30. The bulk score alone tracks observed better: Spearman 0.52 to 0.79.\n\nCALIBRATION TRANSFER (paired minus bulk-only bulk score, fatal donors)\n- Stephenson set -0.47 (p 0.033), MS1 -0.60 (p 0.014), S100 -0.54 (p 0.031).\n- Wave 2: 7 of 8 paired fatal donors vs 5 of 12 bulk-only fatal donors.",
+     "issues": [
+      "CD14 effect sizes are inflated by the spurious CD14 expansion. In generated cells, the CD14 count and the programme score are tightly coupled (within-group Spearman 0.43-0.78; observed 0.02-0.35). With the count held fixed, the bulk-only CD14 severity effect shrinks to 0.12-0.34 (Stephenson set), 0.01-0.47 (MS1) and 0.09-0.21 (S100), and is about 0 in draw 2. On the paired test samples, the count-independent part reproduces only 0.29-0.64x of the observed shift for severity and fatal. The claim's 0.71-0.82 ratio is partly reached through the artefact.",
+      "'All five draws carry the Stephenson set and MS1 in CD14, CD16 and intermediate monocytes in all three contrasts' is overstated. MS1 in CD16 is carried in 2/5 (COVID), 4/5 (fatal, ratio 0.18-0.44) and 4/5 (severity). The discovery agent's own programs_summary.csv says draws 2 and 4 only for fatal. MS1 in intermediate monocytes is carried in 3/5 (COVID) and 0/5 (severity). S100 in CD16 for severity is carried by draw 1 only, not draws 1-2.",
+      "The intermediate-monocyte MS1 gate depends on normalisation. With edgeR prior.count=2 the observed severity effect is p 0.024-0.09; with log2(CPM+1) it is p 0.18-0.45, which makes the gate uninformative. MS1 effect sizes also differ (CD14 severity +0.45..0.74 vs +0.61..1.26).",
+      "The CD16 COVID calibration rests on wave-2 paired samples. Observed wave-1 CD16 monocytes show no COVID rise (MS1 +0.03, S100 -0.03, Stephenson set +0.34, all n.s.), and adjusting for wave removes it. Yet 88 % of bulk-only samples are wave 1, and generated wave-1 CD16 still rises +0.3 to +1.25.",
+      "Fatal cannot be calibrated in wave 1 (1 fatal paired donor), and 7 of 8 paired fatal donors are wave 2. Bulk-only fatal donors have higher bulk programme scores than paired fatal donors (0.47-0.60 log2, p 0.01-0.03), so the calibration may not transfer. Within severe patients, observed CD14 shows no fatal effect (Stephenson set +0.11, p 0.72; 8 vs 5 donors), while generated shows +0.46 to +0.62. The fatal contrast is mostly the severity contrast again, and the generated fatal-beyond-severity CD14 shift is unsupported.",
+      "Not every listed gene matches the observed data. Observed CD14 does not rise with severity for ALOX5AP (-0.03), CYP1B1 (+0.22, p 0.50) or RETN (+0.44, p 0.19), and draw 2 gives CYP1B1 -0.76. The observed-supported genes are S100A8/A9/A12, MCEMP1, PLAC8, CD163, IL1R2 and CLU.",
+      "CD16 and intermediate effects are sensitive to label confidence. Label confidence for these types is 0.01-0.06 lower in severe, fatal and COVID+ samples. With cells of confidence >= 0.99 only, CD16 severity MS1 is 0.03-0.58 (3/5 draws p < 0.05) and S100 0.01-0.57 (0/5). CD14 is robust to this.",
+      "The generated cells add little beyond the bulk, as the claim says. The bulk score alone gives +0.76 to +1.07 (p < 1e-4), and the partial Spearman given bulk is about 0. The S100 CD14 severity increment in draws 0, 3 and 4 is the only reproduced addition.",
+      "Draw agreement is not 5 independent replications: every draw used the same seed 20260927 for its first part. The three programmes overlap: S100 is a subset of the Stephenson set, and the Stephenson set and MS1 share CD163, MCEMP1 and PLAC8. They are not independent evidence."
+     ],
+     "reasoning": "The main effect reproduces with independent code and a different normalisation. In every monocyte type, programme and contrast, the bulk-only generated programme is higher in 5/5 draws. Most contrasts survive the controls I tried:\n- age and sex;\n- mixed-model, GEE and one-sample-per-donor handling of repeated visits;\n- wave-1-only samples;\n- dropping samples of the draw's training donors and the sex-flag sample;\n- days since positive swab;\n- high-confidence labels, granulocyte-like cell exclusion and MS1-state exclusion.\n\nThe CD14 result is calibrated (PASS 5/5 in all contrasts; wave-1 severity PASS 5/5) and agrees in direction with observed paired CD14 cells. The discrete MS1 state is under-generated and the CD14 expansion is spurious, both as claimed. The bulk-baseline numbers and the S100 increment over the bulk also reproduce.\n\nThe claim is still stronger than the evidence:\n1. **Cell number.** The generator ties the programme to its spurious CD14 expansion, a coupling absent in observed cells. Holding the count fixed roughly halves the CD14 effect, removes it in draw 2, and leaves only 0.3-0.6x of the observed shift for severity and fatal.\n2. **CD16 and intermediate MS1 and S100.** Calibration is weaker than stated: several draws are uninformative or low-ratio, and the intermediate-monocyte MS1 severity result depends on normalisation. The CD16 COVID truth exists only in wave-2 paired samples, while bulk-only samples are mostly wave 1.\n3. **Fatal.** It cannot be calibrated in wave 1, its calibration may not transfer, and in observed CD14 it adds nothing beyond severity.\n4. **Gene list.** Three of the listed MS1 genes do not move in observed CD14.\n5. **Beyond bulk.** The generated cells add little to the bulk score.\n\nThe CD14 claim holds, so this is not refuted, but it is qualified on scope (subsets, contrasts, genes) and on effect size. Which draws carry it: CD14 Stephenson set, MS1 and S100 in draws 0-4 for all three contrasts (severity also in wave 1). Draw 2's CD14 MS1 and S100 severity effects are fully explained by the CD14 count.\n\nEverything was CPU only, run under systemd-run with MemoryMax=6G, and all writes stayed in the lens dir. This verifier published nothing to the paper archive; the coordinating agent owns that update.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v00_inspect.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v01_scores.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v01_scores.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v02_meta_checks.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v02_group_covariates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v03_effects.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v03_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v03_effects_wide.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v04_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v04_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v04_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v04_calibration_csv_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v05_bulk_baseline.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v05_bulk_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v05_beyond_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v05_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v06_cells.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v06_cell_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v07_conf_and_coupling.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v07_coupling.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v07_calib_variants.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v07_observed_variants.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v08_count_adjusted_calib_and_genes.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v08_count_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v08_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v09_train_donors_and_fatal_within_severe.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v09_fatal_within_severe.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v09_train_donors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v10_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v10_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_recompute/v10_carry_counts.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "abd3562f5dcb6c69a",
+     "verdict": "qualified",
+     "recomputed": "I rewrote the analysis without importing ip_common, using the same data. Unless noted, settings are edgeR-style log2 CPM with prior 2, at least 10 cells, the lens gene filter, and Welch tests on donor x group means.\n\n(0) Reproduction. Every lens number for the 3 programmes x 3 monocyte types x 3 contrasts x 5 draws matched exactly: generated, observed and bulk-only differences and p-values, max deviation 0.0. The calibration gates also agreed on all non-NA rows (c01b_crosscheck.csv). The claim's headline numbers check out:\n- Draw 0, CD14, Stephenson set, severity: group means 11.21 vs 10.82.\n- Severity effects across draws 0-4: Stephenson set +0.39/+0.42/+0.37/+0.52/+0.55; MS1 +0.45..+0.74; S100 +0.21..+0.39.\n- Lens BH q-values for the same rows: 0.001/0.004/0.011/0.0015/0.0001.\n- Generated/observed ratios on paired test donors: 0.71-0.82 (Stephenson), 0.66-0.80 (MS1), 0.56-0.81 (S100).\n- Observed CD14 monocytes: +0.90 (13 vs 21 donors, p=1e-5); wave 1 only +1.06 (p=0.0025).\n\n(1) CD14 headline is robust.\n- The Stephenson set, MS1 (8 genes) and S100 are carried by draws 0-4 in all three contrasts (lens gate PASS and q<0.05).\n- This holds under log2(CPM+1), at least 20 cells, unfiltered genes, a strict gate (ratio 0.5-2, Spearman >= 0.3) except S100 fatal (4/5), age and sex adjustment, donor-clustered OLS, wave 1 only, without trained donors and without the sex-flag sample.\n- Pooled per-sample calibration in CD14: ratio 0.66-1.11, Spearman 0.47-0.78.\n- In donors with no paired sample at all (100 bulk-only samples), the effect shrinks: severity, Stephenson +0.18..+0.43, with draw 2 at p=0.1.\n\n(2) Exceptions to \"all five draws carry\".\n- MS1 in CD16, fatal: only draws 2 and 4 carry (ratios 0.20-0.31). The lens's own programs_summary.csv also says \"2,4\".\n- MS1 in intermediate monocytes, COVID: draw 1 q=0.054 in the lens's BH family, so 4 of 5 draws carry.\n- MS1 in intermediate monocytes, severity: 0 of 5 draws carry under log2(CPM+1). The observed contrast there is p=0.18-0.45 while the generated one is about 3x larger.\n- S100 in CD16, severity: 2 of 5 draws; 0 of 5 with at least 20 cells or under the strict gate. The observed contrast over all paired samples is p=0.085.\n\n(3) Gene level, CD14 monocytes (c02).\n- ALOX5AP: observed severity +0.05 (p=0.89), fatal +0.26 (p=0.50), COVID -0.22.\n- CYP1B1: observed severity +0.15 (p=0.62), fatal +0.17 (p=0.56), COVID -0.38. For both genes, bulk-only differences are significant in at most 1 of 5 draws and change sign across draws.\n- RETN: observed severity +0.37 (p=0.22); the gate is UNINFORMATIVE.\n- IL1R2: observed +1.70 (p=0.004), but the generated contrast is only 0.1-0.45x of observed.\n- CLU: observed +2.58, PASS in 5/5 draws.\n- Intermediate monocytes:\n  - ALOX5AP: observed -0.63/-0.73 vs generated +0.4..+0.85 (FAIL in draws 0-2, fatal).\n  - CYP1B1, COVID: observed -0.91 (p=0.002) vs generated positive (FAIL 5/5).\n  - MCEMP1: observed +0.02 (p=0.96), yet bulk-only rises +0.25..+0.71 in all 5 draws.\n- The 5 MS1-only genes as a score are UNINFORMATIVE or fail outside CD14.\n\n(4) CD16 monocytes: the rise tracks classical identity genes. With severity, observed CD16 cells gain CD14 +1.05 and VCAN +1.15. After subtracting an identity score (CD14/LYZ/FCN1/VCAN):\n- Observed CD16: Stephenson +0.25 (p=0.23), S100 -0.08 (p=0.74), MS1 -0.03 (p=0.85).\n- Bulk-only CD16 S100 still rises +0.16..+0.44 (4/5 q<0.05), which observed cells do not show.\n- In CD14, the identity-adjusted Stephenson and MS1 scores still PASS 5/5.\n\n(5) Per-cell check, CD14 MS1 genes (c03).\n- Observed severity rise is +1.20 overall and +0.93 in cells outside the MS1 state; the MS1-state fraction rises +7.8 pp (p=0.011).\n- Generated ratio outside the state is 1.05-1.69; the state fraction is reproduced at only 0.03-0.28x. This supports the claim's \"gene-level shift, state under-generated\".\n- High-confidence labels give the same result.\n\n(6) Composition (c05). CD14 monocyte fraction, severity:\n- Bulk-only: +6.8..+12.9 pp in every draw (p<=0.006).\n- Paired test donors: generated +3.0..+9.6 pp vs observed -0.4..+0.6 pp.\n- All observed paired: +0.6 pp (p=0.79). The expansion is spurious, as the claim says.\n\n(7) Does the generated score add to the bulk score? (c04). CD14 S100, severity, likelihood-ratio p:\n- Bulk-only: 0.015/0.175/0.434/0.023/0.004.\n- The same test on each draw's paired test donors with generated cells: 0.383/0.023/0.020/0.462/0.145.\n- With observed cells on the same donors: 0.011-0.027 in all five.\n- The generated Stephenson set and MS1 scores never add in bulk-only (p>=0.06), while the observed Stephenson score adds in all 5 test sets.\n- Per-sample Spearman with observed: the bulk score beats the generated score (S100 0.49-0.57 vs 0.36-0.45). Partial Spearman given the bulk score: -0.14..0.03 for S100, 0.08..0.37 for the Stephenson set.\n\n(8) Fatal is nested inside severity. All 12 fatal bulk-only donors are severe/critical. Within severe/critical patients, observed CD14 shows no fatal effect (8 vs 5 donors): Stephenson +0.11 (p=0.70), MS1 +0.35 (p=0.37), S100 -0.08 (p=0.80). Bulk-only shows +0.40..+0.55 (p<=0.008), following the bulk.\n\n(9) Calibration transfer. Bulk contrasts of the same genes are similar in paired and bulk-only samples:\n- Stephenson severity +0.96 vs +0.76; fatal +0.80 vs +1.07; COVID +0.80 vs +0.83.\n- Bulk-only levels sit 0.06-0.6 log2 higher within groups.\n\n(10) Stephenson's top monocyte severity gene, TNF going down, is not reproduced in bulk-only generated cells: -0.30..+0.11, 0 of 5 draws significant, against observed -0.86 (p=0.006).",
+     "issues": [
+      "The 'all five draws carry' statement is wrong for MS1 in CD16 monocytes, fatal contrast: only draws 2 and 4 carry (generated/observed ratio 0.20-0.31), which is what the lens's own programs_summary.csv says. MS1 in intermediate monocytes, COVID contrast, is carried by 4 of 5 draws in the lens's own BH family.",
+      "The listed MS1 genes are not all raised the way observed monocytes raise them. In observed paired CD14 monocytes, ALOX5AP and CYP1B1 show no severity or fatal association (+0.05 p=0.89; +0.15 p=0.62) and go down with COVID. They are inconsistent across bulk-only draws, significant in at most 1 of 5 with mixed signs. RETN is not significant in observed cells (p=0.22). IL1R2 is under-generated at 0.1-0.45x of observed. In intermediate monocytes, ALOX5AP (fatal) and CYP1B1 (COVID) fail calibration, and MCEMP1 rises in generated cells but not in observed ones. The MS1-programme result therefore rests on CLU, CD163, PLAC8 and MCEMP1, not on the MS1-specific genes.",
+      "'In all three monocyte subsets' overstates CD16. In both observed and generated cells, the CD16 programme rise is matched by a rise of classical-monocyte identity genes inside the CD16 label (CD14 +1.05, VCAN +1.15 observed). After adjusting for identity, observed cells show no CD16-specific rise (Stephenson p=0.23, S100 p=0.74), but bulk-only generated cells still raise S100 (4 of 5 draws q<0.05), so that part is spurious. CD16 S100 for severity is not significant in observed cells (p=0.085).",
+      "Several subset results depend on choices. MS1 in intermediate monocytes for severity is carried by 0 of 5 draws under log2(CPM+1), because the observed contrast is p=0.18-0.45 and the generated one is about 3x larger. CD16 S100 for severity is carried by 0 of 5 draws with at least 20 cells or under a strict gate.",
+      "The 'one association does add' sub-claim fails its own calibration. The draws in which within-CD14 S100 adds to bulk S100 for severity in bulk-only data (0, 3, 4) do not add on their own paired test donors (LR p 0.38, 0.46, 0.15). Draws 1-2 add on test donors but not in bulk-only data. Observed cells add in all five test sets. Partial Spearman of generated vs observed given the bulk score is about 0 (-0.14 to 0.03). The bulk score tracks observed CD14 values better than the generated score in every draw for MS1 and S100.",
+      "Fatal outcome is not an independent finding. All 12 fatal bulk-only donors are severe/critical. Within severe/critical patients, observed CD14 monocytes show no fatal effect (8 vs 5 donors; Stephenson p=0.70, S100 p=0.80), while generated cells show +0.40 to +0.55 by following the bulk. Neither cohort paper reports a monocyte-gene association with fatal outcome.",
+      "Literature nuances. Stephenson 2025 (PDF p.6, journal p.5) compares severity at the time of sampling (56 vs 84 samples), not peak severity, and Fig. 3C (PDF p.7) is total monocytes. Only PLAC8 is reported specifically in CD14 monocytes. TNF loss, the strongest monocyte severity gene in all subsets, is not reproduced in bulk-only generated cells (0 of 5 draws). Gisby 2022 (PDF p.5) attributes the S100A9/A12-containing module tB to neutrophils and low-density granulocytes in the PBMC prep, not to monocytes. Generated CD14 cells carry no neutrophil genes, so this is not label contamination. In Stephenson 2025 (PDF p.11), dexamethasone-induced CD163-high monocytes appear only in the 2021 cohort; the wave-1 checks address this.",
+      "Other caveats: (a) 64 of 164 bulk-only samples come from 29 donors who also have paired samples, and in donors with no paired sample the severity effects shrink to about 60-75%, with draw 2 losing significance. (b) All draws share seed 20260927, so 5/5 draw agreement is not five independent replications. (c) No PBISC-Diffusion archive update was published by this verifier; the coordinator owns that task-end step."
+     ],
+     "reasoning": "(1) Calibration. The programme is not in calibration.csv, so I recomputed the paired check independently. In CD14 monocytes, the Stephenson set, the 8-gene MS1 score and S100 pass in all five draws for COVID, severity and fatal. Effect ratios are 0.56-0.82 for severity, Spearman 0.36-0.77. This survives every normalisation and filter variant, high-confidence labels, wave-1 restriction and identity adjustment. The calibration.csv rows the claim quotes are correct: the MS1 state is under-generated (0.04-0.36x of the observed contrast), and the CD14 composition rise is spurious. Outside CD14 the support is weaker. MS1 in CD16 for fatal is carried by only 2 of 5 draws. Intermediate MS1 and CD16 S100 depend on normalisation and cell-count thresholds. Most important, the CD16 rise is matched by a rise in classical identity genes, so there is no observed CD16-specific programme. At gene level, ALOX5AP and CYP1B1 are not part of the observed shift, and RETN is not significant in observed cells.\n\n(2) Observed support. Strong for CD14 in all three contrasts, including wave 1 only. Fatal within severe/critical patients is not supported by observed cells.\n\n(3) Biology and novelty. Already reported for this cohort and in the COVID literature. Stephenson 2025 PDF p.6 (CD163, S100A8/A9/A12, MCEMP1 up; PLAC8 up in CD14 monocytes; TNF down). Gisby 2022 PDF p.5 (module tB, attributed to low-density granulocytes). Schulte-Schrepping 2020 PDF p.11 (HLA-DR-low S100A-high and CD163-high monocytes, MS1 signature in Fig. 4C). Silvin 2020 PDF p.9 (S100A8/A9 up in severe disease). Reyes 2021 PDF p.3 (MS1 programme higher in CD14 monocytes in severe COVID). COMBAT 2022 PDF p.8 (S100A8/9/12-high cMono and the FKBP5/CD163 module track severity). Nothing contradicts the CD14 part, and nothing is new.\n\n(4) Beyond bulk. Essentially nothing. The bulk score already carries the contrast and predicts observed CD14 values better than the generated score does. The one 'adds to bulk' association (S100, draws 0, 3, 4) fails its own paired calibration.\n\nThe core CD14-monocyte finding is therefore confirmed on all four criteria. Several specific sub-claims are refuted: the all-draws carrying statement, the MS1 gene list, the 'all three subsets' wording for CD16, the S100 'adds' association, and fatal as a separate contrast. Overall verdict: qualified.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c00_inspect.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c00_inspect.log",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c01_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c01_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c01_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c01_pooled.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c01b_crosscheck.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c02_gene_level.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c02_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c02_genes_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c02_adjusted.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c04_beyond_bulk_transfer.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c04_beyond_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c04_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c05_composition.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c05_composition.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c06_new_donor_sensitivity.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c06_new_donor.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c07_quoted_numbers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-3_calib/c07_quoted_numbers.csv",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/SchulteSchrepping2020_Severe_COVID19_dysregulated_myeloid.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Reyes2021_Sepsis_COVID_plasma_induces_suppressive_myelopoiesis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Silvin2020_Calprotectin_abnormal_myeloid_severe_COVID19.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/COMBAT2022_Blood_atlas_COVID19_severity.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-CLIN-1",
+   "lens": "imp_clinical",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 6,
+    "theme": "B lineage",
+    "synthesis_strength": "moderate for the direction of a B-lineage increase in COVID+; 'naive-specific' not supported",
+    "reportable_statement": "Generated cells of bulk-only COVID+ patients contain a larger share of B cells, most of them labelled naive, than those of COVID- patients in all five draws (naive B logit +0.73, +0.71, +1.31, +0.98, +0.93; 1.8-2.7x; total B +0.56 to +1.01), also in wave 1 and in the 45 donors without single cells. Direction is calibrated in 5/5 draws; size in draws 3-4 on all test samples and in 5/5 within wave 1, where the observed truth itself is only nominal. It is a relative-share increase of B cells, not a naive-specific shift.",
+    "carrying_draws": {
+     "direction": "0-4",
+     "magnitude, all paired test samples": "3,4",
+     "magnitude, wave-1 test samples": "0-4 (observed truth p 0.025-0.077)"
+    },
+    "honesty": {
+     "calibration": "calibration.csv COVID row: ratio 0.45/0.56/0.45/0.51/0.86, Spearman 0.54-0.62, generated contrast 0.27-0.69x of observed (gates DIR, DIR, DIR, PASS, PASS). Wave-1 restriction PASS 5/5 (ratio 0.62-1.47, Spearman 0.48-0.64; synthesis i01). In the wave-2 batch the generator reproduces only 0.16-0.49x of the pre-infection to COVID+ rise. Transitional B is under-generated (0.06-0.25x).",
+     "replication": "5/5 draws, q < 1e-3; survives age, sex, ethnicity, diabetes, wave, training donors, sex flag, one sample per donor, label confidence, LDG score, GEE and donor permutation. Weakens with five bulk QC covariates (draw 3 p 0.31); a kitchen-sink model leaves draws 1-2. Shared noise 6.6% of cross-draw agreement.",
+     "observed": "Observed naive B +1.02 logit (p 7e-8) over all samples, mixing processing batches; wave 1 +0.79 (p 0.037); wave-2 batch +1.05 (p 2e-5). In wave 1 the naive share within B does not change (+0.18, p 0.59); total B rises (+0.69, p 0.0014) and the memory share within B falls (-0.87, p 7e-4).",
+     "beyond_bulk": "Partly. Naive-B, pan-B and IGHD bulk markers do not move with COVID (IGHD +0.13 SD, p 0.58), but a ridge model on bulk genes trained on paired samples reproduces the bulk-only shift (+0.76, p 1e-4) and tracks observed naive B better (Spearman 0.73 vs 0.54-0.62); the generated shift left after it is +0.33 to +0.75 (draw 3 p 0.07). Draws 0-3 partly convert the bulk plasmablast signal into naive B."
+    },
+    "verdict_reasons": [
+     "Calibration is partial: direction 5/5, size 2/5 on all test samples.",
+     "Where the paired population matches the bulk-only samples (wave 1) the truth is only nominal (p 0.037) and fragile to one covariate.",
+     "Not naive-specific: the observed wave-1 within-B naive share does not change; it is a B-lineage increase made mostly of naive B.",
+     "The lens's 'total-B bulk score' is 6 of 10 plasma-cell genes; pan-B and IGHD bulk genes are flat.",
+     "Draws 0-3 partly map bulk plasmablast signal onto naive B; observed naive B does not follow it.",
+     "Time range beyond the evidence (persists after day 20, no paired truth); strength should be moderate, not strong."
+    ],
+    "refuted_subclaims": [
+     "the added value is attribution of the B increase to naive B cells",
+     "the total-B bulk score rises (it is a plasma-cell score)"
+    ],
+    "synthesis_note": "IMP-LONG-2 showed that the frozen labeller calls ISG-high switched memory B cells 'naive' (45% at the top ISG quartile; 41-64% of memory labels flip to naive when acute-phase ISG counts are added). The week-1 within-B naive enrichment (+0.83, p 8e-6; verify/IMP-CLIN-1_calib) coincides with peak ISG, so part of the 'naive' attribution is probably this label artefact.",
+    "new_vs_cohort_papers": "Known: Stephenson 2025 PDF p.3 (naive B higher in week-1 COVID+ vs COVID-) and Fig. 2E (PDF p.5; FDR 0.012; week 2 n.s.). Other cohorts report naive B reduced (COMBAT 2022 p.10) or unchanged (Mathew 2020 p.8). New only: bulk-only and no-single-cell donors reproduce it.",
+    "next_steps": [
+     "Restate as a B-lineage share increase within the calibrated window (<=20 days).",
+     "Re-test the naive attribution with the authors' CITE-seq/BCR B labels (as in verify/IMP-LONG-2_calib) or an ISG-robust labeller.",
+     "Report the ridge-adjusted generated residual per draw as the beyond-bulk number."
+    ],
+    "key_sources": [
+     "verify/IMP-CLIN-1_recompute/v02_primary_focus.csv",
+     "verify/IMP-CLIN-1_recompute/v03_confounders.csv",
+     "verify/IMP-CLIN-1_recompute/v04_calibration.csv",
+     "verify/IMP-CLIN-1_recompute/v05_observed.csv",
+     "verify/IMP-CLIN-1_recompute/v06_mechanism.csv",
+     "verify/IMP-CLIN-1_calib/v01_calib_rows.csv",
+     "verify/IMP-CLIN-1_calib/v03_bulk_tests.csv",
+     "verify/IMP-CLIN-1_calib/v03_ridge_pred.csv",
+     "imp_clinical/s02_bulkonly_assoc.parquet",
+     "synthesis_imperial_eskd/i01_codes_by_feature.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-CLIN-1",
+    "cohort": "imperial_eskd",
+    "claim": "Generated cells put a larger share of naive B cells in COVID+ than in COVID− ESKD patients: about 1.8–2.7× by fraction, logit +0.71 to +1.31. This holds in all five draws, in wave 1 alone and in the 45 donors with no single cells. Observed paired cells show the same shift, and a naive-B bulk marker score does not.",
+    "kind": "composition",
+    "feature": "l2 | Naive B cell (supporting: l1 | B cell)",
+    "comparison": "COVID+ vs COVID− across all bulk-only samples (143 vs 19). Repeated in wave 1 only (wave-1 COVID+ vs wave-1 COVID−; every bulk-only COVID− sample is wave 1).",
+    "effect_bulkonly": "Logit coefficients for draws 0–4: +0.73, +0.71, +1.31, +0.98, +0.93. Generated fraction, COVID+ / COVID−: 0.0234/0.0133, 0.0322/0.0177, 0.0261/0.0106, 0.0282/0.0105, 0.0499/0.0269. In SD units: +0.94 to +1.30. Total B cells (l1): +0.56 to +1.01 logit (0.066/0.039 to 0.152/0.083). Wave 1 only: +0.70, +0.71, +1.24, +0.99, +0.74.",
+    "stats": "Model: linear mixed model with a donor random intercept (statsmodels MixedLM, REML, Wald z), logit((n+0.5)/(N+1)) ~ covid_pos + age/10 + male + (1|donor). Data: n = 162 samples from 74 donors. Per-draw p: 1.8e-6, 1.7e-6, 2.0e-8, 8.3e-5, 1.8e-8. BH q over the 50 features in each draw: 4.6e-5, 2.9e-5, 9.8e-7, 8.3e-4, 1.8e-7. Wave 1 only (145 samples, 66 donors): q 2e-4 to 2e-3. Alternative models all give p ≤ 8e-4: arcsine mixed model, donor-clustered OLS, donor-level aggregation, donor-level Mann-Whitney. Sensitivity analyses: (a) without the sex_flag sample; (b) without each draw's training-donor samples (+0.70 to +1.32, p ≤ 8e-5); (c) 45 no-single-cell donors only (97 samples; +0.65 to +1.29, p ≤ 4e-3); (d) adding ethnicity and diabetes (+0.66 to +1.21, p ≤ 2e-3); (e) without the 3 COVID− samples of donors later infected (+0.66 to +1.24, p ≤ 1e-4).",
+    "draws_same_sign": "5/5, all q < 1e-3; the carrying draws are 0–4. Caveat: shared seed noise explains about 6.6% of the cross-draw agreement for naive B (s14), so the 5 draws are not 5 independent replications.",
+    "paired_calibration": "calibration.csv, COVID+ vs COVID− row: ratio 0.45/0.56/0.45/0.51/0.86; Spearman 0.58/0.62/0.56/0.59/0.54; gen diff +0.012/+0.016/+0.019/+0.023/+0.028 vs obs diff +0.044/+0.043/+0.041/+0.041/+0.041. Same sign in 5/5, but the generated contrast is only 0.27–0.69× of observed. Gates: DIR, DIR, DIR, PASS, PASS. Severe vs mild row: the sign is wrong in 0/5 draws (gen −0.010 to −0.017 vs obs +0.008 to +0.013), so no severity claim is made. Recomputed, donor-aware and adjusted, on each draw's test samples: gen +0.39 to +0.99 vs obs +0.92 to +1.28 (p ≤ 2e-6); gates DIR, DIR, DIR, PASS, PASS. Restricted to wave 1: gates PASS, PASS, DIR, PASS, DIR (gen +0.62 to +1.35 vs obs +0.59 to +0.89).",
+    "paired_observed_concordance": "Yes. All 157 observed paired samples, same model: +1.02 logit (p 7.1e-8, q 7.1e-7; 147 samples, 57 donors); fractions 6.4% vs 2.5%. Wave 1 only, same processing protocol: +0.79 (p 0.037; 72 samples, 41 donors); 3.5% vs 1.7%. Total B: +0.81 (p 4e-8); wave 1 +0.69 (p 0.0014).",
+    "beyond_bulk": "What the bulk shows: the naive-B marker score (FCER2, TCL1A, FCRL1, MS4A1, CD22, PAX5 …) shows no COVID difference, either in bulk-only samples (+0.01 SD, p 0.95) or in paired bulk (−0.12 SD, p 0.34). The bulk score still tracks the observed naive-B fraction across samples better than the generated cells do (Spearman 0.64–0.69 vs 0.54–0.62). The total B-cell bulk score is up (+1.01 SD, p 2e-5). What the generated cells add: the generated naive-B shift survives conditioning on the naive-B score (+0.95 to +1.30 SD, p ≤ 4e-5) and on the total-B score (draws 0, 1, 2, 4 p ≤ 2e-3; draw 3 p 0.06). Observed cells show the same beyond-bulk shift: +0.77 SD given the naive-B score (p 4e-9) and +0.66 given the B score (p 3e-6). The generator reproduces this beyond-bulk part on the paired test samples in 5/5 draws (+0.52 to +0.92 SD, p ≤ 9e-6). The added value is therefore attribution: the B-lineage increase is placed in naive B cells. Predictive gain over a full-bulk model was not tested for COVID status.",
+    "novelty": "Known. Stephenson 2025, Cell Genomics (reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf, PDF p. 3) reports higher proportions of naive CD8 T and naive B cells in week-1 COVID+ vs COVID− ESKD samples, and B-ASC up in weeks 1–2. What is new here is only that the shift is reproduced in bulk-only samples, including donors with no single cells.",
+    "strength": "strong",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/c_common.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s02_bulkonly_assoc.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s02_bulkonly_assoc.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s03_paired_calib.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s04_observed_assoc.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s13_extra_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s15_findings_numbers.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s16_extra_checks.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_partial_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s18_extra_covariates.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s19_observed_means.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "acf02e01e8ac38f86",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt every count from the raw cells.parquet files of 11 sets (observed, paired_gen d0-4, bulkonly d0-4). The counts match composition.parquet exactly: 385 blocks, 0 mismatches.\n\n**Primary model, bulk-only (162 samples, 74 donors, 143 COVID+ / 19 COVID-).** Mixed model on logit((n+.5)/(N+1)) ~ covid + age/10 + male + (1|donor).\n- Naive B, draws 0-4:\n  - logit +0.73 / +0.71 / +1.31 / +0.98 / +0.93; p 1.8e-6 / 1.7e-6 / 2.0e-8 / 8.3e-5 / 1.8e-8.\n  - BH q over 50 features: 4.6e-5 / 2.9e-5 / 9.8e-7 / 8.3e-4 / 1.8e-7.\n  - Fractions, COVID+/COVID-: 0.0234/0.0133, 0.0322/0.0177, 0.0261/0.0106, 0.0282/0.0105, 0.0499/0.0269. That is 1.76-2.68x.\n  - SD units: +0.94 to +1.30.\n  - 5/5 draws positive.\n- B cell (l1): +0.63 / +0.56 / +1.01 / +0.82 / +0.88.\n- Every number in the claim reproduces exactly.\n- Independent models agree: GEE +0.71 to +1.29 (p ≤ 3e-4); quasi-binomial with donor-cluster sandwich +0.62 to +1.00 (p ≤ 2.3e-3); donor-level OLS (p ≤ 5.5e-4); Mann-Whitney AUC 0.76-0.80; donor permutation p ≤ 4e-4.\n\n**Confounders.** Naive-B logit for draws 0-4; all 5/5 positive unless noted.\n- Drop sex_flag: unchanged.\n- Drop training-donor samples: +0.70 to +1.32 (p ≤ 4.4e-5).\n- Wave 1 only: +0.70 to +1.24 (p ≤ 1.7e-4).\n- Wave 1 without the w1|w2 pre-infection samples: +0.62 to +1.13 (p ≤ 1.2e-3).\n- Drop C101, C105 and C33 (donors with both statuses): +0.66 to +1.23.\n- Wave-2 covariate: +0.72 to +1.25.\n- The 45 donors with no single cells: +0.65 to +1.29 (p ≤ 4.3e-3).\n- One sample per donor and status: +0.74 to +1.40 (p ≤ 7.7e-5).\n- Unadjusted, age², males only: all hold.\n- Ethnicity, diabetes, DN cause, IHD/COPD, all clinical together: +0.68 to +1.23 (p ≤ 1.5e-3).\n- Bulk library size: unchanged.\n- Generated cell library: +0.68 to +1.00; draw 2 falls to 0.73.\n- Neutrophil/LDG bulk score: +0.60 to +1.12 (p ≤ 1e-3).\n- Days ≤7, 8-14, >14: all positive (draw 3 at >14 days: p 0.075).\n- Peak mild/moderate +0.57 to +1.08; peak severe/critical +0.84 to +1.50; mild at sample +0.62 to +1.09.\n- Naive-B labels with confidence ≥0.9: +0.69 to +1.31. Naive-B confidence does not differ by group (draw 2: -0.025).\n- Leaving out one COVID- donor at a time: minimum coefficient 0.59-1.14. Dropping the 4 lowest COVID- donors: +0.43 to +0.85 (p ≤ 7e-3).\n- Adding 5 bulk QC covariates (MT, RP and unmapped fractions, detected genes, library size): +0.33 / +0.41 / +0.76 / +0.22 / +0.52, with draw 3 p 0.31.\n- Kitchen sink (wave 1, no training donors, no flag, all clinical + bulk QC): +0.29 / +0.50 / +0.74 / +0.22 / +0.16. Only draws 1 and 2 stay significant (p 0.002).\n\n**Calibration.**\n- calibration.csv reproduces exactly (max difference 4e-7). Naive B ratio 0.45/0.56/0.45/0.51/0.86; Spearman 0.54-0.62; the generated contrast is 0.27-0.69x of observed.\n- Official gates: DIR, DIR, DIR, PASS, PASS. B cell: DIR, DIR, PASS, PASS, PASS.\n- NCL / wave-1 test samples:\n  - Raw gates PASS in all 5 draws: ratio 0.62-1.47, Spearman 0.48-0.64, generated difference +0.011 to +0.027 vs observed +0.016 to +0.020.\n  - Logit: generated +0.62 to +1.35 vs observed +0.59 to +0.89, with observed p 0.025-0.077.\n- Cambridge test samples: generated/observed logit ratio only 0.16-0.81.\n\n**Observed paired cells.**\n- All samples, no centre term: +1.02 (p 7.1e-8). With a centre term: +0.97.\n- NCL only: +0.79 (p 0.037; 72 samples, 41 donors). Adding log cell number gives p 0.054. One sample per donor at NCL: +0.96 (p 0.015).\n- Naive B / B share at NCL: +0.18 (p 0.59). Memory B / B at NCL: -0.87 (p 7e-4). B / all at NCL: +0.69 (p 1.4e-3).\n\n**Bulk check.** Bulk-only SD effects:\n- IGHD (never a generator input; Spearman with the observed naive-B fraction 0.70, vs 0.53-0.60 for the generated fraction): +0.13 (p 0.58); paired -0.08.\n- Pan-B genes: +0.16 (p 0.46).\n- Naive-B genes: +0.07 to +0.23 (NS).\n- Plasmablast score: +1.08 (2e-6). IG-transcript fraction: +1.22 (5e-8). IGHM: +1.09.",
+     "issues": [
+      "Calibration is only partial. The official gates on calibration.csv are DIR, DIR, DIR, PASS, PASS. Draw 3 passes only narrowly (level ratio 0.512, contrast 0.57x). On all test samples the generated contrast is 0.27-0.69x of observed. Strictly, draws 3-4 carry magnitude and draws 0-2 carry direction only; the claim's 'carrying draws 0-4' overstates this.",
+      "Where the paired population matches the bulk-only samples (wave 1 / NCL processing), the generator reproduces the contrast: raw gates PASS in all 5 draws, logit +0.62 to +1.35 generated vs +0.59 to +0.89 observed. But the observed truth there is weak: +0.79, p = 0.037 (72 samples, 41 donors); per-draw test subsets p 0.025-0.077. It goes to p 0.054 with log cell number and to p 0.11 with bulk MT or RP fraction. The p = 7e-8 observed result mixes the two processing centres. The large within-Cambridge contrast (+1.04) is reproduced only 0.16-0.81x.",
+      "The 'naive' attribution is not supported. In observed wave-1 cells the naive share within B does not change: +0.18 logit, p 0.59 (0.318 vs 0.303). The shift is in total B (+0.69, p 0.0014), with the memory-B share down (-0.87, p 7e-4). In generated bulk-only cells the within-B naive share moves +0.16 to +0.41, significant in 2/5 draws and in 0/5 within wave 1. This is a B-cell increase made mostly of naive B, not a naive-specific placement. The claim's 'added value is attribution to naive B' is overstated.",
+      "The beyond-bulk text misdescribes the bulk. The discovery agent's 'total B-cell bulk score' (+1.01 SD) is 6/10 plasma-cell genes (JCHAIN, MZB1, TXNDC5, IGLL5, DERL3, TNFRSF17). Pan-B genes (MS4A1, CD79A/B, CD22, BANK1, BLK, PAX5, FCRLA, VPREB3) do not move: +0.16 SD (p 0.46) in bulk-only, -0.03 in paired. Bulk IGHD does not move either: +0.13 SD (p 0.58), -0.08 in paired. IGHD is outside the generator's input and is the best naive-B bulk proxy (Spearman 0.70 with the observed naive-B fraction). The bulk B-lineage rise is IGHM/immunoglobulin/plasmablast transcripts.",
+      "In draws 0-3 the generator partly turns the bulk plasmablast signal into naive B cells. In bulk-only samples, generated naive B rises with the plasmablast score (joint model +0.17 to +0.37, p ≤ 2.3e-4; draw 4 NS). Observed naive B does not (-0.13 to -0.02, NS). The generated-minus-observed residual on test samples increases with the plasmablast score in draws 0-3, significant only in draw 2 (p 0.004). Adjusting for the plasmablast score lowers the bulk-only effect to +0.43 to +0.83 (draw 3 p 0.087), still comparable to observed (+0.77 NCL, +0.88 all).",
+      "Composition-linked bulk covariates weaken the effect. The 5 bulk QC covariates give +0.22 to +0.76, with draw 3 p 0.31. The kitchen-sink model leaves only draws 1 and 2 significant. I treat this as over-adjustment: library size and detected genes change nothing, and MT/RP fractions attenuate the observed single-cell effect about as much. Still, the generated effect is not independent of COVID-wide bulk shifts.",
+      "Text error in the severity calibration: 'the sign is wrong in 0/5 draws' should read 'the sign agrees in 0/5 draws (wrong in 5/5)'. Generated naive B is lower in severe than mild disease, observed is higher. The conclusion of no severity claim stands.",
+      "5/5 draw agreement shows that five LoRA fine-tunes of one base model on identical bulk inputs agree. It is not five independent replications. The 6.6% shared-noise share (s14) was not re-verified here.",
+      "Minor: draw 2's generated cells have a lower library size in COVID+ (-244 UMI, p 1.6e-4). Adjusting for it lowers draw 2 from +1.31 to +0.73 (p 1.4e-4). Generated naive-B cells have lower library sizes in COVID+ in 4/5 draws.",
+      "Strength should be 'moderate', not 'strong'."
+     ],
+     "reasoning": "**Reproduction.** I wrote my own code from the raw cells.parquet files. The 50-feature, per-draw mixed-model analysis reproduces every number in the claim exactly: coefficients, p, BH q, group fractions, SD units, wave-1 results, the no-single-cell donors, the training-donor exclusion, and the observed paired results (+1.02, p 7.1e-8; wave 1 +0.79, p 0.037). I also rebuilt the calibration.csv rows; they match to 4e-7.\n\n**What it survives.** The effect holds in five different model classes. It is unchanged by age, sex, age², males only, ethnicity, diabetes, DN, IHD/COPD, wave (restriction or covariate), the pre-infection samples, the sex_flag sample, training donors, donors with both statuses, the no-single-cell donors, one sample per donor, bulk library size, generated cell library, label confidence (high-confidence labels, confidence by group), and the neutrophil/LDG bulk score. It also holds within every days-since-swab and severity stratum and under leave-one-out of COVID- donors. Generated cell number is fixed at 1,500, so cell number cannot confound. Observed cell number and library do not differ by group. The calibration transfers: within each group, bulk-only wave-1 and paired NCL samples do not differ on any B-relevant bulk metric (all MW p ≥ 0.08).\n\n**Why it is not refuted.** No reasonable confounder removes it in any draw. Only covariates that are themselves composition or disease proxies weaken it, and they weaken the observed single-cell effect about as much.\n\n**Why it is qualified, not confirmed.**\n1. Calibration is partial. Direction holds in 5/5 draws, but magnitude passes in only 2/5 overall (draw 3 narrowly). In the matching wave-1/NCL population the generator does reproduce the contrast, but the observed truth there is only p = 0.037, and it does not survive one more covariate.\n2. It is a total-B shift, not naive-specific. The naive share within B does not change in observed wave-1 cells, so the 'attribution to naive B' adds nothing beyond 'more B cells, most of them naive'.\n3. The claim's bulk statement is inaccurate. Pan-B genes and IGHD, an independent naive-B readout the generator never saw, are flat. The bulk B-lineage rise is plasmablast/IG. Draws 0-3 partly convert that plasmablast signal into naive B, which the observed cells do not.\n\n**Restatement I would accept.** Generated bulk-only cells show more B cells, mostly naive, in COVID+ than COVID- ESKD samples (naive B logit +0.71 to +1.31, 5/5 draws). Direction is calibrated in all draws; magnitude is calibrated in draws 3-4 on all test samples, and in all draws within wave 1, where the observed effect itself is only nominal. The pattern matches observed single cells and is not visible in bulk naive-B or pan-B genes.\n\n**Scope.** CPU only, no GPU work. Files were written only in the verify directory, and no paper-archive update was made from this subagent.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/vc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v00_explore.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v00_explore.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v01_counts.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v01_counts.parquet",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v02_primary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v02_primary.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v02_primary_all.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v02_primary_focus.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03a_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03a_bulk.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03a_bulk.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03b_bulk_raw.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03b_bulk_raw.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03b_bulk_raw.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03_confounders.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03_confounders.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03_confounders.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v03c_proxy.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v06_mechanism.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v07_leakage.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v07_leakage.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v08_ldg.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_recompute/v08_ldg.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "ad1e105f0c6997984",
+     "verdict": "qualified",
+     "recomputed": "CALIBRATION. The calibration.csv rows for l2 Naive B (COVID+ vs COVID-) reproduce exactly. Ratio is 0.45/0.56/0.45/0.51/0.86 and Spearman is 0.58/0.62/0.56/0.59/0.54. Generated diff is +0.012/+0.016/+0.019/+0.023/+0.028 against observed +0.044/+0.043/+0.041/+0.041/+0.041, so the generator gives 0.27/0.36/0.46/0.57/0.69 of the observed contrast. Official gates are DIR, DIR, DIR, PASS, PASS. The pooled row shows the same sign in 5/5 draws.\n\nWAVE 1 ONLY (single-cell batch 1). All 19 bulk-only COVID- and 88% of bulk-only COVID+ samples are in wave 1. Recomputed on wave-1 test samples:\n- ratio 0.67/0.86/0.62/0.80/1.47;\n- Spearman 0.64/0.62/0.49/0.54/0.48;\n- gen/obs diff 0.55/0.72/1.00/1.26/1.59, so PASS in 5/5 draws under the official rule;\n- logit contrast generated +0.72/+0.62/+1.25/+1.35/+0.87 vs observed +0.89/+0.83/+0.69/+0.87/+0.59, with observed p only 0.025-0.077.\n\nWAVE-2 BATCH (wave-2 COVID+ vs the same donors' 2020 pre-infection samples):\n- gen/obs diff is 0.16-0.49, so DIR in 5/5 draws;\n- within donor, observed naive B rises in 9-10 of 11-12 donors; generated rises in 6/11, 6/12, 6/11, 9/11 and 10/11 donors (draws 0-4).\n\nBULK-ONLY, INDEPENDENT REFIT:\n- MixedLM: +0.73/+0.71/+1.31/+0.98/+0.93 (p 2e-8 to 8e-5); the BH q values match the candidate's;\n- GEE: +0.71/+0.72/+1.29/+1.04/+0.90;\n- donor-level permutation: p <= 6e-4;\n- wave 1 only: +0.70 to +1.24;\n- 45 donors with no single cells (none of them training donors): +0.65 to +1.29 (p <= 4e-3);\n- days <= 20 (the time range the paired data cover): +0.72 to +1.36;\n- days > 20 (no paired data, so uncalibrated): +0.58 to +1.02;\n- no covariates: +0.66 to +1.27.\n\nOBSERVED CELLS:\n- all samples: +1.02 (p 7e-8); 6.4% vs 2.5%;\n- wave 1: +0.79 (p 0.037, BH q 0.087, rank 21 of 50); 3.5% vs 1.7%;\n- wave-2 batch: +1.05 (p 2e-5); 8.7% vs 3.7%.\n\nNAIVE SHARE WITHIN B (observed):\n- wave 1, naive / non-ASC B: +0.38 (p 0.22);\n- wave 1, naive / l1 B: +0.18 (p 0.59);\n- week 1: +0.83 (p 8e-6);\n- week 2: +0.22 (p 0.29).\n\nTRANSITIONAL B: generated level ratio is 0.06-0.25, and the COVID contrast fails in 5/5 draws, while observed transitional B rises (+0.88 logit).\n\nBULK ALONE:\n- pan-B (non-ASC) score: +0.12 z (p 0.51);\n- naive-B genes: +0.07 (p 0.68);\n- MS4A1, CD79A, TCL1A, FCER2, FCRL1 individually: all p >= 0.16;\n- the candidate's 'total B' score is 6 of 10 plasma-cell genes (+0.81 z, p 2e-5); plasma genes alone: +1.07 (p 4e-6).\n\nGENERATED SHIFT AFTER ADJUSTING FOR BULK SCORES:\n- pan-B: +0.66 to +1.22;\n- pan-B + T + LDG: +0.44 to +0.85 (p <= 2e-3);\n- observed cells, same adjustment: +0.88.\n\nRIDGE BASELINE (2,000 bulk genes predicting the observed naive-B logit, trained on the paired samples):\n- cross-validated Spearman 0.73, vs 0.54-0.62 for the generator;\n- predicted bulk-only COVID shift: +0.76 (p 1e-4); wave 1: +0.57 (p 0.011);\n- generated shift left after adjusting for the ridge prediction: +0.33/+0.38/+0.75/+0.37/+0.52 (p 5e-3/2e-3/1e-4/0.07/1e-4).\n\nTRANSFER: within each group, bulk-only and paired bulk have similar B-gene profiles (p 0.21-0.92).\n\nINVERSE-VARIANCE SUMMARY: all 5 draws +0.86; PASS draws 3-4: +0.95. Naive SE; the draws are not independent.",
+     "issues": [
+      "Calibration supports the direction in 5/5 draws but not the size on the full trust layer. The generated COVID difference is only 0.27-0.69x of observed; official gates are DIR, DIR, DIR, PASS, PASS, so only draws 3 and 4 carry the magnitude there. The quoted '1.8-2.7x' generated fold is calibrated only in the wave-1 restriction.",
+      "The single-cell processing batch confounds the observed headline (6.4% vs 2.5%) and the calibration.csv row. Stephenson 2025 STAR Methods (PDF pp. e2/e5): wave 1 was library-prepped in Newcastle; wave-2 COVID+ samples and their 2020 pre-infection controls ('w1|w2') were processed with wave 2 and prepped in Cambridge. Naive B is 8.7% in wave-2 COVID+ against 3.5% in wave-1 COVID+.",
+      "In wave 1, where the bulk-only samples are, the observed support is only nominal: +0.79 logit, p 0.037, BH q 0.087 (rank 21 of 50); on each draw's test set p is 0.025-0.077. The generator passes there in 5/5 draws, but draws 2-4 overshoot the observed logit contrast by 1.5-1.8x.",
+      "In the wave-2 batch the generator reproduces only 0.16-0.49x of the observed pre-infection to COVID+ rise. Within donors, draws 0-2 match the direction only at chance level: 6/11, 6/12 and 6/11 donors up, against 9-10 of 11-12 in observed cells.",
+      "The attribution claim is not supported. The candidate's 'total B-cell bulk score (+1.01 SD)' is 6 of 10 plasma-cell genes (JCHAIN, MZB1, TXNDC5, IGLL5, DERL3, TNFRSF17). A pan-B non-plasma bulk score is flat (+0.12 z, p 0.51), so the bulk shows no B-lineage increase to 'place in naive B cells'.",
+      "The observed naive share within B does not change in wave 1: +0.38 logit (p 0.22) for naive / non-ASC B and +0.18 (p 0.59) for naive / l1 B. Observed within-B naive enrichment is a week-1 effect: +0.83 (p 8e-6) in week 1, +0.22 (p 0.29) in week 2.",
+      "The generator barely produces transitional B (level ratio 0.06-0.25) and misses its COVID rise (FAIL in 5/5 draws, observed +0.88 logit). Its 'naive B' is best read as naive plus transitional B. Per the reference INDEX, Stewart 2021 shows RNA cannot separate transitional from naive B.",
+      "The 'beyond bulk' value is limited. Marker scores miss the shift, but a plain ridge regression of the observed naive-B fraction on bulk genes, trained on the paired samples, reproduces the bulk-only COVID shift (+0.76 logit, p 1e-4; wave 1 +0.57, p 0.011). It also tracks observed naive B better than the generator (Spearman 0.73 vs 0.54-0.62). After adjusting for the ridge prediction the generated shift shrinks to +0.33 to +0.75 (draw 3 p 0.07).",
+      "The time range goes past the evidence. Stephenson 2025 reports naive B up in week 1 only: text on PDF p. 3; Fig. 2E on PDF p. 5, FDR 0.012, with week 2 not significant. The generated shift persists in week 3 and later and at days > 20 (+0.58 to +1.02, 20 COVID+ samples), beyond the paired COVID+ range (max day 20) and without published support.",
+      "Novelty: already known for this cohort (Stephenson 2025). It is a shift in the relative PBMC share. In other COVID cohorts, mass cytometry found fewer naive B cells in frequency and number (COMBAT 2022, PDF p. 10). Mathew 2020 found naive B unchanged (PDF p. 8). Gisby 2022 wave-2 flow cytometry found no major change in overall lymphoid proportions within PBMC (PDF p. 6). Do not phrase this as more naive B cells in absolute terms.",
+      "The 5/5 draw agreement is not 5 independent replications. All draws share the base model and seed 20260927; shared slot noise explains 6.6% of agreement for naive B, consistent with s14. Strength should be 'moderate', not 'strong'."
+     ],
+     "reasoning": "Not refuted. Neither the calibration nor the observed cells contradict the claim, and the cohort paper supports it. It is qualified because the support for its size and for its interpretation is partial.\n\n(1) Calibration. The calibration.csv rows reproduce exactly. All 5 draws keep the sign, but the generator compresses the contrast to 0.27-0.69x of observed. Gates are DIR, DIR, DIR, PASS, PASS, so the direction is carried by draws 0-4 and the size only by draws 3-4. Following the guide, I recomputed the trust layer on the restriction that matches the bulk-only samples: single-cell batch 1, wave 1, which holds all bulk-only COVID- samples. There all five draws pass the official rule, and the bulk-only generated group fractions closely match the generated fractions on wave-1 test samples. The observed wave-1 contrast they are matched against is weak, though (p about 0.03-0.08). In the wave-2 batch the generator is under-responsive, and draws 0-2 fail the within-donor pre-infection to COVID+ test.\n\n(2) Observed cells. The shift is real in observed paired cells overall (+1.02 logit) and inside each batch: wave 1 +0.79 (p 0.037), wave-2 batch +1.05 (p 2e-5, with about 10 of 12 donors rising). The candidate's headline 6.4% vs 2.5% mixes the two processing batches.\n\n(3) Biology and novelty. Stephenson 2025 already reports a higher naive-B proportion in week-1 COVID+ ESKD samples (PDF p. 3 text; Fig. 2E on PDF p. 5, FDR 0.012; week 2 not significant). So the finding is known; what is new is only that bulk-only and no-single-cell donors reproduce it. Gisby 2022 flow cytometry (PDF p. 6) does not resolve B subsets. Other COVID cohorts depend on the reference frame: COMBAT 2022 (PDF p. 10) reports naive B reduced, Mathew 2020 (PDF p. 8) reports it unchanged. The claim should therefore be stated as a relative PBMC-share shift limited to the calibrated time range (days <= 20).\n\n(4) Beyond bulk. Confirmed that B-cell marker gene levels in the bulk do not move with COVID, so the generated shift says more than a marker score. The candidate's 'attribution' story is wrong, though: their 'total-B' bulk score is mainly plasma-cell genes, and the observed within-B naive share does not change in wave 1. A supervised ridge model trained on the paired samples recovers most of the bulk-only shift, and the generator adds only a partial residual on top of it.\n\nIndependent refits all reproduce the bulk-only effect: GEE, donor permutation, no covariates, wave 1 only, no-single-cell donors and days <= 20. The candidate's numbers check out; the 'total-B' label and the 'attribution' interpretation are what need correcting, plus a downgrade from strong to moderate.\n\nI wrote only to the verify/IMP-CLIN-1_calib directory: CPU only, every Python run inside a 6G memory scope, no GPU work. I prepared no paper-archive update; that is the coordinating agent's decision.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v03_ridge_pred.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v04_extra.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v04_extra.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v04_markers.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v05_covariates.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-1_calib/v05_covariates.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/features/imperial_eskd/calibration.csv",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/COMBAT2022_Blood_atlas_COVID19_severity.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Mathew2020_COVID_immunotypes_CD38_HLADR_T_cells.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-PROG-2",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 7,
+    "theme": "beyond the bulk (method)",
+    "synthesis_strength": "moderate for COVID+ vs COVID- (draws 0-4, strict test passes); not supported for severity or fatal",
+    "reportable_statement": "For COVID+ vs COVID-, the double-centred within-type logFC pattern of the generated bulk-only cells agrees with observed paired cells beyond a bulk broadcast and csSAM in all five draws, including on bulk-only donors with no paired sample (L2 r 0.105-0.156) and in a strict test (donor-disjoint, wave 1, sex flag dropped, training donors removed: r 0.05-0.09, p 0.005 in 5/5). It captures 4-8% of the reliable observed variance. This replicates SEP-PROG-4 for disease vs control only, at about half the sepsis strength.",
+    "carrying_draws": {
+     "COVID+ vs COVID-": "0-4",
+     "severity": "none independent of shared patients",
+     "fatal": "none (draw 1 donor-disjoint only, not in the strict test)"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv. Paired-test L2 r: COVID 0.146-0.196, severity 0.094-0.148, fatal 0.071-0.087 (11 types); passes 5/5, but for fatal a bulk broadcast through the observed per-type structure matches or beats it in 4/5 draws.",
+     "replication": "5/5 positive for COVID at L2 and coarse level; the draws' double-centred matrices correlate 0.25-0.30 (shared seed).",
+     "observed": "Observed split-half reliability 0.26 (COVID), 0.14-0.16 (severity), ~0.21 (fatal, ~10 types); r/sqrt(reliability) 0.20-0.28 but r^2/reliability only 4-8%.",
+     "beyond_bulk": "Yes for COVID, modestly: partial r after pipeline broadcasts and atlas predictors 0.10-0.13 (p 0.003 donor-disjoint, 5/5); csSAM double-centred r -0.02 to 0.09. A broadcast through the same log-CPM pipeline is not zero (r 0.03-0.15). Content is ISG-led (13-33% of covariance) and mostly between lineages (within-lineage r 0.01-0.09)."
+    },
+    "verdict_reasons": [
+     "Severity and fatal agreement is carried by the 29 donors present in both sets (12-13 of 14 observed severe, 6-7 of 8 fatal donors): donor-disjoint severity r 0.04-0.06 (0/5 above the donor-label null), fatal 0.03-0.06 (1/5).",
+     "Mechanism is donor-fingerprint transfer: same-donor within-type deviations correlate 0.17-0.20 vs ~0 for other donors.",
+     "'A bulk broadcast has zero double-centred structure by construction' is false through the log-CPM pipeline.",
+     "'About a quarter of the reliable signal' is r/sqrt(SB); the share of reliable variance is 4-8%.",
+     "Normalisation-sensitive (prior count 0.5: r 0.06-0.09; pseudocount-free on >=100 CPM genes 0.31-0.46).",
+     "Wave confounds the observed COVID contrast; wave-1 or wave-adjusted r 0.03-0.10, still 5/5.",
+     "Some 'cell-type-specific' genes are contamination-like (NKG7 in CD14 monocytes; HLA-DP in naive CD4)."
+    ],
+    "refuted_subclaims": [
+     "shared across the COVID, severity and fatal contrasts",
+     "captures about a quarter of the reliable observed signal",
+     "a bulk broadcast has zero structure by construction"
+    ],
+    "new_vs_cohort_papers": "Methodological; replicates SEP-PROG-4 for disease vs control. The biology (ISG-led, monocyte emphasis) is in Stephenson 2025 and Gisby 2022.",
+    "next_steps": [
+     "Make the donor-disjoint, wave-matched double-centred r with a pipeline-broadcast null the standard beyond-bulk metric.",
+     "Report it on well-expressed genes with a pseudocount-free estimator.",
+     "Severity/fatal versions need paired donors who are not in the bulk-only set."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-2_recompute/v12_strict.csv",
+     "verify/IMP-PROG-2_recompute/v03_overlap.csv",
+     "verify/IMP-PROG-2_recompute/v07_fingerprint.csv",
+     "verify/IMP-PROG-2_recompute/v04_broadcast.csv",
+     "verify/IMP-PROG-2_calib/v09_summary.csv",
+     "verify/IMP-PROG-2_calib/v02_overlap.csv",
+     "verify/IMP-PROG-2_calib/v05_share.csv",
+     "verify/IMP-PROG-2_calib/v08_disjoint_beyond_bulk.csv",
+     "verify/IMP-PROG-2_calib/v04_decomposition.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-2",
+    "cohort": "imperial_eskd",
+    "claim": "The bulk-only generated cells carry a cell-type-specific (double-centred) disease-response pattern that matches the observed paired cells, beyond both a bulk broadcast and a bulk deconvolution (csSAM). This replicates the sepsis result SEP-PROG-4 in a second cohort. The pattern is modest: it is shared across the COVID, severity and fatal contrasts (not specific to one), and it captures about a quarter of the reliable observed signal.",
+    "kind": "other",
+    "feature": "Within-type logFC matrices (donor-aware) after removing each gene's mean and each cell type's mean (double-centring). Two versions: 12 AIFI L2 types x 1,655 genes expressed in all of them, and 5 coarse types (Monocyte, T, NK, B, DC) x 1,920 genes.",
+    "comparison": "COVID+ vs COVID-, severe vs mild (COVID+) and fatal vs survived (COVID+). Bulk-only draw j against all observed paired samples; and generated vs observed on each draw's paired test samples.",
+    "effect_bulkonly": "L2 double-centred r, bulk-only vs observed, draws 0-4: COVID 0.141, 0.136, 0.104, 0.139, 0.107; severity 0.112, 0.103, 0.084, 0.098, 0.095; fatal 0.115, 0.090, 0.070, 0.090, 0.100. Coarse version: COVID 0.20-0.30, severity 0.04-0.20, fatal 0.12-0.35. Top contributing genes: ISGs (IFITM1, IFIT1/2/3, RSAD2, EPSTI1); monocyte genes (PLAC8, S100A8/9, NCF1, LYZ, CST3); HLA-DPA1/DPB1; NK cytotoxic genes (NKG7, GNLY, CCL5); and some immediate-early genes. Gene-level check: take the bulk-only within-type DE genes (q < 0.05, in type x draw cells that pass the calibration gate) whose direction is opposite to a significant bulk change. These are 1,343 gene x type x draw tests for COVID, 275 for severity and 830 for fatal. In them, the observed cells go the generated way in 70 %, 76 % and 91 % of cases (same direction with p < 0.05 in 18 %, 12 % and 56 %).",
+    "stats": "Pearson r over the flattened double-centred matrices. Null 1: 200 gene-label permutations, p = 0.005 (the floor) in every draw and contrast; the null's 97.5th percentile is 0.012-0.022. Null 2: 50 donor-label permutations within the bulk-only samples; null mean -0.01 to 0.01, 95th percentile 0.03-0.10, p 0.02-0.04. Observed split-half reliability (Spearman-Brown, 20 splits): 0.25 for COVID and 0.15 for severity; not estimable for fatal. So r divided by the square root of the reliability is 0.21-0.29.",
+    "draws_same_sign": "5/5 draws positive in all three contrasts, at both L2 and coarse level, and above both nulls. The shared-seed caveat applies.",
+    "paired_calibration": "On paired test samples, L2 r: COVID 0.146-0.196; severity 0.094-0.148; fatal 0.075-0.081 (only draws 3-4 could be computed, because the other test sets have fewer than 3 fatal donors in some types). Coarse r: 0.28-0.33 (COVID), 0.17-0.34 (severity), 0.20-0.34 (fatal). For the gene-level check on paired tests, observed cells go the generated way in 75 %, 71 % and 80 % of bulk-discordant genes. This is not a calibration.csv feature.",
+    "paired_observed_concordance": "The bulk-only matrices correlate with those from all usable observed paired samples (values above). Without the three monocyte types r is 0.04-0.09; without the top 50 genes it is 0.05-0.10; without sex-chromosome and immediate-early genes it drops by at most 0.01. Cross-contrast check: the generated COVID pattern correlates with the observed severity pattern at 0.10-0.12 and with the observed fatal pattern at 0.06-0.11, almost as high as with the observed COVID pattern (0.10-0.14). So the shared content is a general inflammation pattern.",
+    "beyond_bulk": "Yes, but modestly. A bulk broadcast has zero double-centred structure by construction. csSAM on the same bulk gives double-centred r of -0.01 to 0.09 with NNLS fractions, and -0.02 to 0.03 even with the observed (oracle) fractions on paired tests. The partial r of generated given csSAM equals the generated r. Caveats: for COVID monocyte genes that go against the bulk, the observed cells go the generated way in only 10-50 % of cases; ribosomal-protein genes go the wrong way; the fatal bulk-discordant genes are mostly long genes. Within-type logFC also correlates with genomic gene span in observed fatal (0.14-0.24) and in generated COVID for draws 0-2 (0.07-0.21). That is a technical axis.",
+    "novelty": "Methodological. It replicates SEP-PROG-4 from REPORT_ye2026_sepsis.md (sepsis bulk-only r 0.17-0.35, paired 0.13-0.21) in the Imperial cohort, against a stronger csSAM baseline. The sepsis report named this replication as the condition for calling SEP-PROG-4 a confirmed method result.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05_localisation.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/loc_double_centred.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05b_deconv_baselines.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_withintype_compare.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_fraction_check.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05c_double_centred_detail.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_reliability.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_robustness.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_type_contrib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_top_cells.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05d_dc_sensitivity.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_sensitivity.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s05e_dc_specificity.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/dc_specificity.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s08_gene_level_replication.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/gene_replication.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/gene_replication_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/gene_bulk_discordant.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s09_technical_axes.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/tech_length.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a45756ccfcfdb4f36",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt everything from the compact files with my own code. The claim's numbers reproduce exactly.\n\nReproduction:\n- L2 double-centred r, bulk-only vs all observed paired, draws 0-4:\n  - COVID: 0.141, 0.136, 0.104, 0.139, 0.107\n  - severity: 0.112, 0.103, 0.084, 0.098, 0.095\n  - fatal: 0.115, 0.090, 0.070, 0.090, 0.100\n- Coarse r: COVID 0.20-0.30, severity 0.04-0.20, fatal 0.12-0.35.\n- Paired-test L2 r: COVID 0.146-0.196, severity 0.094-0.148, fatal 0.081 and 0.075 (draws 3-4 only).\n- Observed split-half reliability: 0.26 (COVID), 0.14 (severity); L2 fatal not estimable.\n\nDonor-label null (200 permutations):\n- L2: p ≤ 0.02 in all 15 draw x contrast cells.\n- Coarse severity: p = 0.035, 0.095, 0.075, 0.338, 0.070, so only draw 0 passes.\n- Coarse fatal: draw 3 fails (p = 0.16).\n\nDonor-disjoint test (bulk-only donors who have no paired sample, 47 of 76 donors):\n- COVID L2: 0.130, 0.141, 0.109, 0.134, 0.105, p = 0.01 in 5/5 draws. Coarse COVID: 0.21-0.32.\n- Severity L2: 0.040, 0.055, 0.040, 0.056, 0.039, p = 0.07-0.18 (0/5).\n- Fatal L2: 0.052, 0.064, 0.035, 0.044, 0.037 (draw 1 p = 0.01; the others p = 0.08-0.24).\n- Coarse severity: -0.08 to 0.04. Coarse fatal: -0.05 to 0.12. Both 0/5.\n- Bulk-only donors who DO have paired samples give higher agreement: L2 severity 0.084-0.119 (p ≤ 0.03 in 5/5), coarse severity 0.14-0.28, coarse fatal 0.22-0.39.\n- Random donor subsets of the same size give higher severity/fatal r than the donor-disjoint subset in every draw (mean L2 severity 0.067-0.085).\n\nDonor-fingerprint transfer:\n- A donor's generated within-type deviation (from its bulk-only visits) correlates with the same donor's observed deviation (from its paired visits).\n- Coarse, genes ≥ 100 CPM: 0.17-0.20 for the same donor vs about 0 for other donors (p = 0.0005 in every draw). It holds without the training donors.\n\nStrict combined test (donor-disjoint, wave 1, sex_flag sample dropped, observed without draw j's training donors):\n- COVID L2: 0.091, 0.094, 0.053, 0.077, 0.056, p = 0.005 in 5/5 draws. A bulk broadcast gives -0.01 to 0.02 here.\n- COVID coarse: 0.10-0.23, p ≤ 0.035 in 5/5.\n- Severity: L2 0.024-0.041 (1/5 draws pass); coarse 0/5.\n- Fatal (wave restriction impossible): L2 draws 3-4 give 0.034-0.036 (p = 0.09-0.12); coarse 0/5.\n\nBulk broadcast passed through the same log-CPM (prior 2) pipeline (deterministic):\n- L2 r: COVID 0.077-0.119, severity 0.064-0.123, fatal 0.074-0.150. This is as large as the generated cells.\n- Partial r of generated given broadcast: COVID 0.093-0.127, severity 0.076-0.099, fatal 0.058-0.100.\n\nNormalisation sensitivity (COVID L2):\n- Prior count 0.5: r drops to 0.06-0.09. Prior count 5: r rises to 0.15-0.20.\n- Pseudocount-free estimator (ratio of linear means) on the 1,655 genes: 0.023-0.052.\n- Same estimator on genes ≥ 100 CPM: 0.31-0.46, where the broadcast gives -0.21 to 0.06.\n\nWave:\n- Wave-1 only, L2 COVID: 0.051-0.100 (p ≤ 0.02).\n- Age + sex + wave adjusted: 0.033-0.102.\n\nGene-level bulk-discordant check (my types and gate; share where observed cells go the generated way):\n- COVID: 60% of 778 tests, against a 38% base rate. Donor-disjoint: 68% of 668, against 42%.\n- Fatal: 89% of 462, against a 56% base rate. Donor-disjoint: only 9 tests remain.\n- Severity: 65% of 57, against 46%. Donor-disjoint: 0 tests.\n\nLabel confidence (coarse, from the 200-cell subsample, COVID):\n- High-confidence cells only: 0.19-0.235. All cells: 0.185-0.216. Random cells of the same number: 0.15-0.20.\n\nOther checks:\n- Cross-donor paired calibration (COVID L2, split halves): same half 0.088-0.121, cross half 0.074-0.107.\n- Bulk COVID / severity / fatal contrast, paired vs donor-disjoint bulk-only: 0.88 / 0.64 / 0.49. For overlapping donors it is 0.74 / 0.90 / 0.86.",
+     "issues": [
+      "Severity and fatal agreement is carried by donors who appear in both sets. 29 of the 76 bulk-only donors also have paired samples, including 13 of the 14 observed severe donors and 7 of the 8 observed fatal donors. Restricting the bulk-only side to donors with no paired sample halves the L2 r (severity 0.04-0.06, fatal 0.035-0.064) and takes it below the donor-label null in 5/5 (severity) and 4/5 (fatal) draws. Coarse r falls to about 0 (0/5 draws for both). Same-size random donor subsets keep higher r, so this is not just lost power. The claim that the pattern is 'shared across the COVID, severity and fatal contrasts' does not survive the donor repeated-measures control.",
+      "The mechanism is donor-fingerprint transfer, not disease response. The generator puts each donor's own within-type deviations, taken from that donor's bulk at other visits, into the generated cells. Same-donor r is 0.17-0.20 at coarse level vs about 0 for other donors (p = 0.0005 in all draws), and it holds without training donors. These shared fingerprints inflate the agreement for small contrasts such as fatal and severity.",
+      "The statement 'a bulk broadcast has zero double-centred structure by construction' is false for the claimed statistic. A deterministic broadcast (generated baselines and libraries, bulk fold change applied to every type) run through the same log-CPM prior-2 pipeline gives L2 r 0.06-0.15, as large as the generated cells. The cause is type-specific prior-count compression of fold changes. The generated signal is mostly separate from this artefact (partial r is close to raw r), and in the strict wave-1 donor-disjoint test the broadcast is about 0. So 'beyond broadcast' holds for COVID, but the raw r is not a clean measure.",
+      "The statistic depends strongly on normalisation. For COVID L2, prior count 0.5 cuts r by about 35% and prior count 5 raises it to 0.15-0.20. A pseudocount-free estimator on the claimed 1,655 genes gives only 0.02-0.05. The real signal sits in well-expressed genes (≥ 100 CPM: 0.31-0.46).",
+      "Wave is a confounder. In the observed set, 55% of COVID+ samples are wave 2 (the dexamethasone era) against 0% of COVID- samples. For fatal, 90% vs 42% are wave 2 in the observed set and 45% vs 6.5% in the bulk-only set. Wave-1-only or wave-adjusted COVID r drops by 30-60% (to 0.03-0.10) but stays significant in 5/5 draws. Fatal cannot be tested within wave 1 (only 3 observed fatal wave-1 samples). Age + sex + wave adjustment brings fatal L2 down to 0.037-0.057.",
+      "The donor-label null was never run at coarse level. There, coarse severity passes in only 1 of 5 draws (p = 0.035, 0.095, 0.075, 0.34, 0.07) and coarse fatal draw 3 fails (p = 0.16). So '5/5 draws above both nulls at both L2 and coarse level' is overstated.",
+      "The gene-level fatal figure (claimed 91%; 89% in my recompute) rests on overlapping donors and a high base rate. Across all bulk-significant gene x type cells, the observed within-type sign already opposes the bulk 56% of the time. Donor-disjoint, only 9 testable cells remain; severity has 0. The COVID gene-level result holds: 68% against a 42% base rate, and 19% against 5.5% at p < 0.05.",
+      "Calibration does not transfer for severity and fatal. Paired-test calibration reproduces and is mostly cross-donor (split halves keep about 70-90% of the same-sample r). But at bulk level, the donor-disjoint bulk-only severity and fatal contrasts match the paired ones at only 0.64 and 0.49, against 0.90 and 0.86 for overlapping donors. calibration.csv has no row for this feature. Its composition rows for the 12 types are moderate: Spearman 0.12-0.65, with cDC2 and CD56dim NK at 0.34 or below, and ratios 0.34-2.3.",
+      "Shared-seed caveat: all draws use the same seed, the same bulk inputs and the same observed reference. Their double-centred matrices correlate 0.25-0.30 (L2) and 0.42-0.43 (coarse), so '5/5 draws' is not five independent replications.",
+      "Ruled out as drivers: age and sex (r changes by 20% or less), the sex_flag sample (1 sample, no change), training donors (changes of 25% or less), per-type and common-depth thinning (r rises), label confidence (high-confidence-only cells keep or raise r), and HLA or sex-chromosome genes (changes of 0.005 or less)."
+     ],
+     "reasoning": "Verdict: qualified. All numbers reproduce exactly from my own code, but only the COVID part survives the confounder controls.\n\nThe COVID contrast (COVID+ vs COVID-) holds up. All five draws carry it in the strictest test: donor-disjoint, wave 1, sex_flag sample dropped, and draw j's training donors removed from the observed reference. There, L2 r is 0.05-0.09 (p = 0.005 in every draw) and coarse r is 0.10-0.23. A bulk broadcast is about 0 in that setting, and the result is at least as strong with pseudocount-free estimates on well-expressed genes. This is roughly 40-60% of the claimed size. It does replicate the disease-vs-control result of SEP-PROG-4.\n\nSeverity and fatal fail. They are not reportable as disease-response agreement: no draw carries severity once donors are disjoint, and only draw 1 carries fatal, and not in the strict test. Their agreement comes from donors whose other visits are in the observed set. I showed the transfer directly: same-donor within-type deviations match far better than other-donor ones. The wave confounding of fatal cannot be removed with these data.\n\nThe 'beyond bulk' justification is wrong as written. Through the same log-CPM pipeline, a broadcast matches the generated raw r at the claim's settings. The generated cells still add a separate component (partial r is close to raw r), so the conclusion survives for COVID but the argument needs replacing.\n\nThe four honesty checks:\n1. Calibration: the paired-test r reproduces and is mostly cross-donor, but it does not transfer to donor-disjoint severity or fatal, and calibration.csv has no row for this feature.\n2. Replication across draws: 5/5 for COVID, subject to the shared-seed caveat; 0-1/5 for severity and fatal once donors are disjoint.\n3. Agreement with observed cells: modest for COVID. In the strict test, r divided by the square root of the reliability is about 0.10-0.18.\n4. Beyond the bulk: yes for COVID, by partial r and by the strict test where the broadcast is about 0.\n\nWhat I did not do: I started no GPU work, wrote only in my verify directory, and did not publish an archive update; that is the coordinator's decision.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/vc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v00_explore.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v00_side_covariates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v00_donor_overlap.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v01_recompute.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v01_recompute.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v01_reliability.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v01_draw_similarity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v02_confounders.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v02_side_tech.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v03_overlap_random.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v03_adjust.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v04_broadcast_baseline.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v05_artefact.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v05_artefact.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v05_type_prior.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v06_clean_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v06_clean_tests.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v07_donor_fingerprint.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v09_calibration.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v12_strict.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_recompute/v12_strict.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "aba604e5fae876d47",
+     "verdict": "qualified",
+     "recomputed": "My own code (vlib.py, same definitions) reproduces every number in loc_double_centred.csv exactly (abs diff 0.0000). All values below are double-centred (DC) r at L2 (12 types x 1,655 genes), per draw 0-4.\n\n1) PAIRED-TEST CALIBRATION. The DC feature is not in calibration.csv, so I recomputed it on each draw's own test samples, with a 500-permutation donor-label null on the generated labels.\n- COVID: 0.196, 0.170, 0.147, 0.148, 0.146 (p 0.002-0.004).\n- Severity: 0.119, 0.094, 0.148, 0.101, 0.124 (p 0.002).\n- Fatal: 0.071, 0.081, 0.087, 0.081, 0.075 (p 0.002-0.016). Draws 0-2 use 11 types, so fatal is computable in all draws, not only draws 3-4.\n- The gate passes in 5/5 draws for all three contrasts.\n- Fatal caveat: a bulk broadcast pushed through the observed per-type structure gets 0.116, 0.096, 0.095, 0.049, 0.103 on the same samples. That is at or above the generated r in draws 0, 1, 2 and 4.\n\n2) BULK-ONLY VS OBSERVED, with a 1,000-permutation donor-label null.\n- All bulk-only samples: COVID 0.104-0.141 (p 0.001); severity 0.084-0.112 (p 0.001-0.014); fatal 0.070-0.115 (p 0.001-0.018).\n- Donor overlap: 29 of the 76 bulk-only donors also have paired samples. They include 12 of the 14 observed severe donors and 6 of the 8 observed fatal donors.\n- Donor-disjoint bulk-only samples (donors with no paired sample):\n  - COVID 0.146, 0.156, 0.120, 0.141, 0.114 (p 0.002, 5/5 draws);\n  - severity 0.039, 0.054, 0.038, 0.055, 0.039 (p 0.06-0.23, 0/5);\n  - fatal 0.051, 0.064, 0.031, 0.042, 0.037 (p 0.008 in draw 1 only; others 0.06-0.18).\n- Size-matched random donor subsets would give severity 0.067-0.083 and fatal 0.052-0.078. The disjoint values sit at the 4-19th percentile for severity.\n- Overlapping donors alone (smaller set): severity 0.088-0.120 (p 0.004-0.012); fatal 0.070-0.112.\n- Bulk-level check: the disjoint donors' own bulk contrast agrees less with the paired bulk contrast (severity 0.64, fatal 0.50) than the overlapping donors' does (0.90, 0.86).\n- Without the draw's training donors: COVID 0.086-0.138, severity 0.072-0.088, fatal 0.036-0.099. Fatal falls most in draws 1-2, where fatal donor C101 trained the model.\n- Dropping the one sex_flag sample changes nothing.\n\n3) BULK-DERIVABLE BASELINES, which the claim says are about 0 \"by construction\".\n- Bulk logFC broadcast through the real per-type depth and prior-count pipeline (bulk-only vs observed):\n  - through the generated structure: COVID 0.055-0.080, severity 0.036-0.044, fatal 0.049-0.094;\n  - through the observed structure: 0.068, 0.031, 0.099.\n- Atlas-weighted bulk predictor (bulk logFC x relative log expression): 0.095, 0.067, 0.040.\n- Partial r of the generated pattern after all baselines: COVID 0.096-0.131, severity 0.081-0.106, fatal 0.063-0.103.\n- On donor-disjoint samples:\n  - COVID partial r 0.096-0.136 (300-permutation null p 0.003, 5/5 draws);\n  - severity 0.037-0.054 (p 0.08-0.22);\n  - fatal 0.029-0.062 (p 0.007 in draw 1 only). The fatal generated r there (0.03-0.06) is below the observed-structure broadcast (0.104).\n\n4) OBSERVED RELIABILITY (50 donor split-halves, Spearman-Brown).\n- SB is 0.267 for COVID and 0.157 for severity. For fatal it is not estimable on 12 types; on about 10 types it is 0.215.\n- Correlation with the reliable part (r/sqrt(SB)) is 0.20-0.28.\n- The share of reliable variance (r^2/SB, the convention the sepsis report used for \"5-16%\") is only 4.0-7.4% for COVID and 4.5-7.9% for severity. On donor-disjoint samples, severity falls to 1.0-2.0%.\n\n5) WHAT CARRIES THE AGREEMENT.\n- Interferon hallmark genes (78) carry 13-33% of the covariance. Removing them lowers r by 0.01-0.03.\n- Within-lineage r is 0.01-0.09. Within the monocyte subtypes it is 0.08-0.20; within CD4 T and within B it is about 0.\n- \"Foreign\" cells (a gene in a type that expresses it at under 10% of its top type; 4.5% of cells) carry 6-18% of the covariance, and r there (0.07-0.27) is higher than in the other cells. Examples:\n  - NKG7 agreement is 92% in CD14 monocytes;\n  - CCL5 is about 94% foreign;\n  - HLA-DPA1/DPB1 agreement for severity and fatal is 70-75% from naive CD4 T cells;\n  - LYZ and S100A9 appear in CD4 T cells for fatal.\n- Cross-draw DC r is 0.25-0.30, so the five draws share structure (shared seed).\n\n6) GENE-LEVEL CHECK. Of the bulk-discordant genes, 96-98% are bulk-down genes that the generator calls up within a type. The base rate for this direction (observed within-type logFC goes against a significant bulk-down change) is already 54%, 61% and 69%. Against that, the claimed 70%, 76% and 91% are lifts of +16, +15 and +21 points, not lifts over 50%. Type-wide offsets drive part of it: the observed T-cell median within-type logFC is +0.19 for COVID and +0.37 for fatal.",
+     "issues": [
+      "Severity and fatal 'replication' in bulk-only samples rests on the same patients. 12 of the 14 observed severe donors and 6 of the 8 observed fatal donors also contribute bulk-only samples (other visits). On donor-disjoint bulk-only samples, severity DC r is 0.038-0.055 (donor-label p 0.06-0.23, 0/5 draws) and fatal is 0.031-0.064 (p < 0.05 only in draw 1). These are below size-matched random donor subsets (severity mean 0.067-0.083). The overlapping donors alone give 0.088-0.120. 'Shared across the COVID, severity and fatal contrasts' and '5/5 draws above both nulls' do not hold as independent replication for severity and fatal.",
+      "'A bulk broadcast has zero double-centred structure by construction' is true only for logFC values. Pushed through the real per-type depth and prior-count pipeline, a pure bulk broadcast correlates with the observed DC pattern at 0.03-0.10. An atlas-weighted bulk predictor reaches 0.04-0.095, which beats the claim's csSAM baseline (-0.01 to 0.09). For fatal, the broadcast (0.099-0.104) matches or exceeds the generated r. That holds for bulk-only on all samples (draws 1-3) and on disjoint donors (5/5 draws), and for the paired-test calibration in draws 0, 1, 2 and 4.",
+      "'Captures about a quarter of the reliable observed signal' overstates it. r/sqrt(SB) = 0.20-0.28 is a correlation with the reliable part. The share of reliable variance (r^2/SB, the convention the sepsis report used for SEP-PROG-4's '5-16%') is 4-8% for COVID and severity, and 1-2% for severity on donor-disjoint samples.",
+      "The fatal calibration is not limited to draws 3-4. On the 11 types with at least 3 donors per side, draws 0-2 give 0.071-0.087 (p <= 0.016). But in 4/5 draws it does not exceed a bulk broadcast through the observed structure. The observed fatal reliability is about 0.21, and only on about 10 types.",
+      "The gene-level check is compared to 50% implicitly. The direction-conditional base rate (observed within-type logFC goes up for a significantly bulk-down gene) is already 54%, 61% and 69%, so the claimed 70%, 76% and 91% are lifts of +16, +15 and +21 points. 96-98% of the bulk-discordant genes are bulk-down genes called up within a type, which fits lymphopenia composition plus type-wide offsets (observed T-cell median within-type logFC +0.19 for COVID, +0.37 for fatal). The check also shares the donor overlap problem for severity and fatal.",
+      "Part of the 'cell-type-specific response' is contamination-like signal that the generator reproduces. Gene x type cells expressed at under 10% of the gene's top type are 4.5% of cells but carry 6-18% of the covariance, with higher r. The 'NK cytotoxic genes' agree mostly in monocyte types: NKG7 92% in CD14 monocytes, CCL5 about 94% foreign. For severity and fatal, HLA-DPA1/DPB1 agreement comes 70-75% from naive CD4 T cells rather than the known monocyte HLA-II loss. LYZ and S100A9 appear in memory CD4 T cells for fatal.",
+      "Specificity is mostly between lineages. Within-lineage r is 0.01-0.09; within monocyte subtypes it is 0.08-0.20, and within CD4 T and B subtypes it is about 0. Interferon hallmark genes carry 13-33% of the covariance.",
+      "Training-donor leakage reduces the effect. Excluding each draw's training donors from both sides gives COVID 0.086-0.138, severity 0.072-0.088 and fatal 0.036-0.099. Fatal falls most in draws 1-2, where the training set includes fatal donor C101.",
+      "'5/5 draws' is not five independent replications: the generated DC patterns of different draws correlate at 0.25-0.30 (shared seed 20260927). The gene-label null (97.5th percentile 0.012-0.022) is anti-conservative about 3-4x against the donor-label null (95th percentile 0.051-0.076)."
+     ],
+     "reasoning": "Verdict: qualified. The COVID+ vs COVID- part holds. The severity and fatal parts, the \"zero by construction\" premise and the \"quarter of the signal\" wording do not.\n\n(1) Calibration. The DC feature has no row in calibration.csv, so I recomputed it per draw on that draw's own paired test samples, with a donor-label null. It passes in all five draws for all three contrasts: COVID 0.146-0.196, severity 0.094-0.148, fatal 0.071-0.087. The claim says fatal could only be computed for draws 3-4; on 11 types, draws 0-2 can be calibrated too. For fatal, though, a bulk broadcast pushed through the observed per-type structure correlates with the observed pattern as well as or better than the generated cells in 4/5 draws. So the fatal calibration does not show content beyond bulk.\n\n(2) Observed support. The observed DC pattern has low reliability (SB 0.27 for COVID, 0.16 for severity, about 0.21 for fatal on reduced types). The generated cells explain only 4-8% of its reliable variance, so \"a quarter\" overstates it. For COVID, agreement holds on bulk-only donors who have no paired samples (0.114-0.156, p 0.002 in 5/5 draws). For severity and fatal, most observed case donors are the same patients as the bulk-only case donors. On donor-disjoint samples the agreement is not significant (severity 0/5 draws, fatal 1/5). The disjoint donors' own bulk contrasts also diverge from the paired ones. So the severity and fatal \"replication\" is within-patient across visits, not generalisation to new patients.\n\n(3) Biology and novelty. The COVID pattern is ISG-led with a monocyte emphasis. Stephenson 2025 (Cell Genomics) reports IFN-alpha/beta response enrichment \"across a broad range of innate and adaptive immune cells\" (journal p. 3, Fig. 2K). For severity it reports 125 of 205 severity genes in monocytes, with HLA-DPB1/DPA1/DRB1/DRA/DQA1 down and S100A8/9/12, MCEMP1, CD163 and PLAC8 up in monocytes (journal p. 5, Fig. 3C). It notes a paucity of severity associations in T cells (journal p. 13). Gisby 2022 (Nat Commun) reports bulk ISG pathways and a severity granulocyte/low-density-granulocyte module containing NCF1, NCF2, S100A9 and S100A12 (p. 5), and SIGLEC-1 restricted to CD14+ monocytes (p. 7). Schulte-Schrepping 2020 describes HLA-DRlo S100Ahi monocytes in severe COVID (pp. 2, 11).\n- The biology is therefore already reported and not new for this cohort. The novelty is only methodological.\n- Several \"named\" genes are contamination-like rather than a cell-type response (NKG7 and CCL5 in monocytes; HLA-DP, LYZ and S100A9 in CD4 T cells).\n- The fatal agreement sits in Treg and naive CD4 ISG cells, a T-cell signal Stephenson does not report.\n- No comparison was made with established methods for cell-type-specific DE from bulk (CIBERSORTx, bMIND, TCA). Jaakkola 2022 (p. 2) reports high precision and low recall for such methods; Meng 2023 (p. 5) reports that performance drops for lowly expressed genes.\n\n(4) Beyond bulk. For COVID, yes, modestly: the partial r after pipeline broadcasts and atlas predictors is 0.10-0.13 (p 0.003 on donor-disjoint samples, 5/5 draws). For severity it holds only on the full, overlapping sample set. For fatal it does not beat a broadcast.\n\nDraws that carry a result: COVID, draws 0-4. Severity: none independently of the shared patients. Fatal: at most draw 1, which does not replicate.\n\nAs a replication of SEP-PROG-4 (sepsis vs healthy), only the COVID+ vs COVID- contrast replicates, and at about half the sepsis strength: bulk-only r 0.10-0.16 here vs 0.17-0.35, and 4-7% of reliable variance vs 5-16%.\n\nAll work was CPU-only (MemoryMax=6G scopes) and written only to the verify directory. This verifier published no archive delta; any archive update is left to the coordinator.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_calib/v08_disjoint_beyond_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_calib/v10_named_gene_cells.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_calib/v10_named_gene_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_calib/papers/Stephenson2025.pages.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-2_calib/papers/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pages.txt",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-POP-1",
+   "lens": "imp_populations",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 8,
+    "theme": "monocyte HLA class II x interferon",
+    "synthesis_strength": "moderate for the composite fraction's associations (calibrated 5/5); not a coupled cell state; nothing beyond bulk",
+    "reportable_statement": "The share of generated CD14 monocytes that are both ISG-high and HLA-DR-low is higher in bulk-only COVID+, severe/critical and higher-WHO samples, calibrated in all five draws, as in observed cells. It is the overlap of an ISG expansion and a global HLA-II decline, not a coupled IFN+ HLA-II-low state, and the bulk carries every association at least as well. The severity effect is acute (<=14 days: +0.11 to +0.16, 5/5).",
+    "carrying_draws": {
+     "COVID, severity, WHO (composite fraction)": "0-4",
+     "fatal within severe": "none",
+     "47 bulk-only donors with no paired sample, severity": "none"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv; recalibrated on COVID+ tests: ratio 0.72-1.20, Spearman 0.81-0.85, signs 5/5 for COVID, severity, fatal and WHO. The joint state is not calibrated: HLA-low share over-produced 1.25-1.44x with Spearman 0.17-0.50; HLA-low among ISG+ cells Spearman -0.01 to 0.39. Level ratio near 1 comes from opposite errors.",
+     "replication": "5/5 draws (donor means: severity p 0.0034-0.020); survives age/sex/wave/days, sex flag, training donors, first sample per donor, label confidence and thinning. Unadjusted sample-level severity is 0/5 because severe bulk-only samples are later (median day 15 vs 9.5); within <=14 days 5/5. Adjusting for the generated CD14 share removes it, but removes the real bulk ISG10 association too (over-adjustment). Shared noise: same-slot OR 15-31 vs 3-6 shifted.",
+     "observed": "Observed COVID +0.149 (p 3e-7), severity +0.180 (p 0.0011; wave 1 +0.155, p 0.0096), fatal +0.214 (p 0.0017), WHO +0.094 per grade (p 4e-5). The HLA-low shift is larger in ISG- cells (+0.215, p 0.003) than in ISG+ cells (+0.073, p 0.29); excess over independence tracks neither severity (p 0.57) nor WHO (p 0.27); composite AUC 0.839 vs product of marginals 0.835.",
+     "beyond_bulk": "None: bulk ISG10-HLA2 gives severity p 0.0066, fatal p 0.0004, WHO p < 1e-4; HLA2 alone severity p 1e-4; LR p for adding the fraction 0.46-0.99."
+    },
+    "verdict_reasons": [
+     "The 'subset / sub-state' reading is contradicted by observed cells (global HLA-II decline; no coupling).",
+     "The quoted 0.42 vs 0.27 HLA-low share among ISG+ cells depends on keeping samples with 1-4 ISG+ cells.",
+     "Not significant in the 47 bulk-only donors with no paired sample (severity p 0.12-0.53).",
+     "Fatal is severity restated (adjusted p 0.33-0.87 generated; 0.66 observed); WHO is mostly between donors (within-donor 1/5).",
+     "Calibration transfer imperfect: bulk-only severe samples are later, have lower bulk ISG and are 88% wave 1.",
+     "Novelty overstated; 'ESKD differs from non-ESKD' contradicted by both cohort papers."
+    ],
+    "refuted_subclaims": [
+     "a coupled IFN-stimulated HLA-II-low sub-state",
+     "attribution beyond the bulk",
+     "ESKD-specific pattern"
+    ],
+    "new_vs_cohort_papers": "Components reported for this cohort: Stephenson 2025 Fig. 5B (IFN-stimulated CD14 expanded in severe/critical) and Fig. 5C (lower HLA-DR protein on IFN-stimulated CD14), PDF p.11; Fig. 3C; Gisby 2022 Fig. 3c (PDF p.6) and SIGLEC-1 pp.7-8. Stephenson p.12 and Gisby p.8: COVID effects not ESKD-specific.",
+    "next_steps": [
+     "Report the ISG axis and the HLA-II axis separately, not the overlap.",
+     "Calibrate HLA-II per cell before any joint-state claim.",
+     "State severity within <=14 days."
+    ],
+    "key_sources": [
+     "verify/IMP-POP-1_recompute/tables/v07_verdict_numbers.csv",
+     "verify/IMP-POP-1_recompute/tables/v04_windows.csv",
+     "verify/IMP-POP-1_recompute/tables/v06_proxy.csv",
+     "verify/IMP-POP-1_recompute/tables/v03_calib.csv",
+     "verify/IMP-POP-1_calib/tables/v02_decomposition_contrasts.csv",
+     "verify/IMP-POP-1_calib/tables/v02_component_calibration.csv",
+     "verify/IMP-POP-1_calib/tables/v04_auc_composite_vs_marginals.csv",
+     "verify/IMP-POP-1_calib/tables/v03_components_by_donor_subset.csv",
+     "imp_populations/tables/s12_shared_noise.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-POP-1",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only ESKD COVID samples, the generated CD14 monocytes contain a larger IFN-stimulated, HLA class II-low subset in COVID+ vs COVID-, in severe/critical vs mild/moderate, with higher WHO grade at sampling and in fatal cases. The observed paired cells contain the same subset with the same associations. The fraction adds no outcome information beyond bulk ISG and HLA-DR expression.",
+    "kind": "state",
+    "feature": "Share of CD14-labelled monocytes with >=4 of 10 ISGs detected (ISG15, IFI6, IFI27, IFI44L, MX1, OAS1, IFIT1, IFIT3, RSAD2, SIGLEC1) and HLA-DRA+HLA-DRB1 < 23.1 CP10k (the 25th percentile of observed COVID- CD14 monocytes). The rule is embedding-free and applied to all cells via markers.npz, with observed cells thinned to each draw's generated CD14 median library. Joint-embedding counterpart: Mono r2.0 c12, a fragment of the stable ISG-high CD14 cluster r1.0 c0. Shared up markers (generated and observed, excluding the rule's own genes): IFITM1/2/3, MT2A, PLAC8, XAF1, EPSTI1, RNASE2, APOBEC3A, TYMP, MX2/OAS. Shared down markers: HLA-DPA1/DPB1, HLA-DRB5, CD74 and AP-1/IEG genes (JUNB, IER2, FOS, ZFP36). About half of these cells are CD163+. Level: observed native 14.3% of CD14; observed thinned 11.0-14.1%; paired_gen 10.8-15.6%; bulk-only 9.3-13.7% per draw.",
+    "comparison": "Severe/critical vs mild/moderate peak severity (COVID+; 28 vs 27-30 donors). Fatal vs non-fatal (COVID+; 12 vs 43-46 donors). WHO grade at sample (slope over 143 COVID+ samples). COVID+ vs COVID- (58 vs 19 donors).",
+    "effect_bulkonly": "Severity, donor means severe vs mild by draw: d0 0.143 vs 0.083 (+0.061), d1 0.165 vs 0.094 (+0.071), d2 0.199 vs 0.116 (+0.083), d3 0.174 vs 0.079 (+0.096), d4 0.166 vs 0.093 (+0.074). Fatal: +0.145 to +0.190 (e.g. d4 0.267 vs 0.092). WHO grade: +0.046 to +0.066 per grade. COVID+ vs COVID-: +0.092 to +0.119 (e.g. d1 0.130 vs 0.010). Fatal within severe/critical: +0.144 to +0.188, not supported (see paired columns).",
+    "stats": "Donor-level Mann-Whitney on donor means within each draw; BH across the 17 rule features within each draw x contrast. Severity p 0.0034-0.020 (q 0.007-0.072; q<0.1 in 5/5 draws). Fatal p 0.0005-0.0056 (q 0.002-0.016). WHO-grade slope: OLS with donor-clustered SE, p 0.0001-0.010. COVID p 0.0001-0.017. Adjusted model (frac ~ severe + days since swab + age + sex + wave, donor-clustered OLS): +0.090 to +0.116, p 0.0002-0.015 (5/5); fatal adjusted +0.088 to +0.114, p 0.009-0.085 (3/5 below 0.05). Wave 1 only: severity +0.048 to +0.088, p 0.010-0.035 (5/5). Sensitivity (sex-flag sample and the draw's training-donor samples removed): severity p 0.006-0.030, fatal p 0.0007-0.008. Cluster-level screen (Mono r2.0 c12): severity q<0.1 in 5/5 draws.",
+    "draws_same_sign": "5/5 for COVID, severity, fatal and WHO grade, and the gate passes in all 5 draws. Caveat: draws share sampler noise. For this state the same-slot odds ratio across draws is 18.9, against 4.4 at shifted slots, and the per-sample fractions correlate 0.82 across draws, so the 5 draws are not independent replications.",
+    "paired_calibration": "New feature, recalibrated on each draw's paired test samples with the contrast's own restriction (COVID+ for severity and fatal). Ratio 0.70/0.91/1.16/0.75/0.79 on all test samples and 0.73/0.94/1.18/0.78/0.80 on COVID+. Spearman 0.78/0.77/0.81/0.76/0.78 (all) and 0.79/0.83/0.87/0.81/0.84 (COVID+). Severity contrast, generated vs observed: +0.146/+0.149/+0.201/+0.147/+0.111 vs +0.151/+0.142/+0.148/+0.202/+0.137, same sign 5/5. Fatal: +0.09 to +0.19 vs +0.16 to +0.21 (5/5). WHO grade: +0.046 to +0.080 vs +0.073 to +0.107 (5/5). COVID: +0.11 to +0.16 vs +0.11 to +0.15 (5/5). Caveat: the level ratio near 1 partly comes from errors that cancel. ISG-high CD14 is under-produced (0.60-0.81x) and HLA-II-low CD14 is over-produced (1.26-1.42x). For reference, the calibration.csv rows for 'ISG-high monocyte' give ratio 0.65-0.81, Spearman 0.78-0.85 and both contrasts 5/5.",
+    "paired_observed_concordance": "Yes. Observed native cells, all 157 paired samples, donor level: COVID +0.149 (p=3e-7; 34 vs 32 donors). Severity +0.180 (p=0.0011; 13 vs 21); wave 1 only +0.155 (p=0.0096); adjusted +0.154 (p=0.003). Fatal +0.214 (p=0.0017; 8 vs 26); adjusted +0.161 (p=0.017). WHO grade +0.094 per grade (p=4e-5). Fatal within severe: +0.137 (p=0.13; 8 vs 5), not significant, and the generated within-severe contrast is about 0 in draws 3-4. Among ISG-high CD14 cells, the HLA-low share is 0.42 in severe vs 0.27 in mild (p=0.026) and rises with WHO grade (p=1e-4). ISG+ cells that keep HLA-II do not track WHO grade (+0.024, p=0.48).",
+    "beyond_bulk": "Little. The generated fraction correlates with bulk ISG10 (Spearman 0.77-0.84) and with bulk ISG10-HLA2 (0.78-0.87). Donor-level severity AUC: generated fraction 0.68-0.73, bulk ISG10-HLA2 0.70-0.75, 4-score bulk baseline 0.83-0.84. Adding the fraction to the bulk baseline gives LR p 0.66-0.89 (severity) and 0.19-0.66 (fatal). With the extended baseline (adds HLA-DP/DQ/DM/CD74 and CD16-monocyte genes) LR p is 0.50-0.98. Even the observed single-cell fraction adds nothing (p 0.63/0.18). What the cells add is attribution: they place the bulk ISG-high/HLA-DR-low severity signal in one CD14 sub-state. It is not a positively coupled state: the within-sample observed/expected ratio is 0.75-0.87 (log OR -0.2 to -0.5), so ISG-high cells are not preferentially HLA-low.",
+    "novelty": "Partly reported. Stephenson 2025 Cell Genomics 5:100918 reports, as separate observations: IFN-stimulated CD14 monocytes (Fig 4A, p.7), a greater early IFN response in severe ESKD COVID (p.8), and HLA class II downregulation in monocytes with severity (Fig 3C, p.6; steep HLA class II decline in severe disease, p.8). It does not report an IFN+ HLA-II-low subset. Schulte-Schrepping 2020 Cell 182:1419 (p.1427) links ISG-high monocytes to HLA-DRhi CD83hi cells of mild disease, and HLA-DRlo CD163hi monocytes to early severe disease. COMBAT 2022 Cell 185:916 (p.922) finds the IFN response in milder disease and HLA-DR loss of classical monocytes with severity. In this ESKD cohort, the IFN-stimulated monocytes of severe patients are HLA-DR-low, which matches Stephenson 2025 but differs from the non-ESKD mild-ISG pattern. A combined subset that tracks the WHO grade has not been described as such.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s07_rules.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_results.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07b_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_thresholds.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s15_finding_numbers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_increment.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08b_increment_extended.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_spearman.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s09_coupling_results.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s09_coupling_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s13_rule_markers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s13_rule_marker_concordance.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s12_shared_noise.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s06_stability.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s02_profile_Monocyte_r2.0.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s04_summary_r2.0.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a9080137424aec1f6",
+     "verdict": "qualified",
+     "recomputed": "Own code (v01-v07) on the raw compact files (cells.parquet, markers.npz, meta.csv, bulk npz). Discovery scripts were used only to locate inputs.\n\nTHRESHOLD AND LEVELS\n- The HLA-low threshold reproduces exactly: T_LO = 23.086 CP10k (9,735 CD14 cells from 34 COVID- paired samples).\n- Levels: observed native 14.3%; bulk-only 9.3/10.3/13.7/10.7/10.9%; paired_gen 11.0/11.2/15.6/11.4/10.8%.\n\nBULK-ONLY EFFECTS (donor means, Mann-Whitney U; identical to the claim)\n- Severity by draw: +0.061/+0.071/+0.083/+0.096/+0.074, p 0.017/0.012/0.020/0.0034/0.011.\n- COVID: +0.092 to +0.119, p 0.0001-0.017.\n- Fatal: +0.145 to +0.190, p 0.0005-0.0056.\n- WHO slope (clustered OLS): +0.046 to +0.066, p 0.0001-0.010.\n- Sensitivity set (no sex-flag sample, no training-donor samples): severity p 0.006-0.030.\n- Wave 1 only: severity p 0.010-0.035.\n- Clinically adjusted (days, age, sex, wave): +0.090 to +0.116, p 0.0002-0.015.\n- Fatal adjusted: 3/5 draws below 0.05.\n\nROBUSTNESS\n- First sample per donor: +0.10 to +0.12, p 0.004-0.018 (5/5).\n- Only CD14 cells with L2 confidence ≥0.95: 5/5.\n- Each cell thinned to 500 UMIs: 4/5 (draw 3 p=0.066).\n- At least 50 CD14 cells per sample: 3/5 (draw 1 p=0.21, draw 2 p=0.39).\n- Permutation test of the donor mean difference: p 0.016-0.13 (2/5).\n\nTIMING (bulk-only design imbalance)\n- Severe bulk-only samples: median day 15, 55% after day 14. Mild: day 9.5, 10% after day 14. Paired samples: day 9 vs day 9.\n- Sample-level severity, unadjusted: +0.03 to +0.06, p 0.08-0.49 (0/5).\n- Same model plus log days: +0.088 to +0.108, p 0.0006-0.011 (5/5).\n- Within ≤14 days: +0.112/+0.128/+0.157/+0.142/+0.134, p 0.0006-0.007 (5/5).\n- After day 14: 1/5 draws significant. Observed cells show the same time pattern.\n\nCD14 ABUNDANCE\n- Severe samples get about 2x more generated CD14 cells (p<0.01 in every draw).\n- This is not calibrated: paired CD14-share severity contrast is +0.03 to +0.10 generated vs -0.004 to +0.006 observed.\n- Adjusting for log CD14 share: donor level 0/5 below 0.05; with days 2/5; acute first sample plus days 5/5 (p 0.0007-0.038).\n- Negative control: the same adjustment also removes the severity association of the real bulk ISG10 score (acute first sample p 0.013-0.022 becomes 0.026-0.45). So the share is acting as a proxy for severity, i.e. over-adjustment.\n- Generated CD14 share tracks bulk neutrophil genes (rho 0.42-0.58); observed share does not (rho 0.00-0.09).\n\nSTATE-CELL CONTENT\n- Generated state cells in COVID+ samples carry essentially no granulocyte genes (≤0.4%; observed 1.4-8.5%). Platelet genes ≤3%.\n- CD163+: 22-56% generated vs 36-41% observed.\n\nOWN CALIBRATION (COVID+ test samples vs thinned observed)\n- State fraction: ratio 0.72/0.96/1.20/0.78/0.79; Spearman 0.81/0.82/0.85/0.81/0.84.\n- Severity contrast, generated: +0.146/+0.149/+0.201/+0.147/+0.111.\n- Severity contrast, observed: +0.154/+0.132/+0.144/+0.202/+0.146.\n- Same sign 5/5 for COVID, severity, fatal and WHO grade. The gate passes in 5/5 draws.\n- Components:\n  - ISG-high: ratio 0.63-0.81, Spearman 0.79-0.86.\n  - HLA-low: ratio 1.25-1.44, Spearman 0.18-0.48.\n  - HLA-low among ISG+ cells: ratio 1.07-1.50, Spearman 0.09-0.39.\n- calibration.csv 'ISG-high monocyte' rows: ratio 0.65-0.81, Spearman 0.78-0.85, both contrasts 5/5.\n\nFATAL AND WHO GRADE\n- Fatal adjusted for peak severity plus clinical covariates: p 0.33-0.87 in bulk-only; observed p=0.66.\n- Fatal within severe, generated vs observed: +0.055/+0.072/+0.062/-0.007/+0.001 vs +0.10 to +0.14.\n- Fatal patients' samples were taken earlier on average (8.4 vs 12.0 days, p 0.08-0.12).\n- WHO grade within donors: +0.031 to +0.054, positive in 5/5 but p<0.05 in 1/5. Between donors: rho 0.40-0.50.\n\nOBSERVED TRUTH (reproduced exactly)\n- COVID +0.149 (p=3e-7); severity +0.180 (p=0.0011); fatal +0.214 (p=0.0017); fatal within severe +0.137 (p=0.13).\n- WHO grade +0.094 per grade (p=4e-5); within donors +0.065 (p=0.031).\n- HLA-low share among ISG+ cells: 0.417 vs 0.272 (p=0.026).\n- Adjusting for CD14 share changes nothing in observed cells.\n\nBEYOND BULK\n- Generated fraction vs bulk ISG10, donor level: rho 0.83-0.92.\n- Likelihood-ratio p for adding the fraction to bulk ISG10 + HLA2: 0.77-0.95 on donor means; 0.03-0.29 on the acute first sample (1/5 below 0.05).\n\nOTHER\n- Per-sample fractions correlate 0.72-0.89 across draws.\n- Pooled within-sample observed/expected ratio: 0.88-0.95 bulk-only, 0.93 observed. The claim says 0.75-0.87; the conclusion is the same.\n- Bulk ISG/HLA profiles of paired and bulk-only samples match within group, except severe ≤14 d: bulk-only is 0.8 log2 lower in ISG10 and 0.3 lower in HLA2.",
+     "issues": [
+      "Timing imbalance in the bulk-only design. Severe bulk-only samples are later (median day 15, 55% after day 14) than mild ones (day 9.5, 10%); paired samples are balanced (9 vs 9). The unadjusted sample-level severity effect is +0.03 to +0.06 with p 0.08-0.49 (0/5). The donor-level headline (+0.06 to +0.10) is diluted by late samples. The effect is an acute-phase one: ≤14 d gives +0.11 to +0.16 (5/5), >14 d is about 0 (1/5), as in observed cells. The claim should say this.",
+      "Generated CD14 abundance is not calibrated. The generator makes about 2x more CD14 cells in severe samples; the paired CD14-share severity contrast is +0.03 to +0.10 generated vs about 0 observed. The generated share tracks bulk neutrophil genes (rho 0.42-0.58) but observed share does not. Adjusting for log CD14 share removes the donor-level severity effect (0/5 below 0.05), and requiring ≥50 CD14 cells loses draws 1 and 2. The negative control shows this is over-adjustment: the same adjustment removes the real bulk ISG10 severity association. It holds in the acute first sample with days adjustment (5/5). Still, within bulk-only samples the state shift cannot be fully separated from the generator's monocyte over-production.",
+      "The HLA-II-low half of the definition is weakly calibrated per sample. Spearman generated vs observed is 0.18-0.48 for the HLA-low share and 0.09-0.39 for HLA-low among ISG+ cells. Levels are over-produced (1.25-1.44x and 1.07-1.50x). The state's good sample-level calibration (Spearman 0.81-0.85) rests on its ISG part; only the group-level HLA contrasts reproduce (same sign 5/5). The level ratio near 1 comes from errors in opposite directions, as the claim notes.",
+      "The fatal association is not independent of peak severity. Fatal adjusted for severity plus clinical covariates has p 0.33-0.87 in bulk-only (0/5) and p=0.66 in observed cells. Fatal patients' samples were taken earlier (8.4 vs 12.0 days). Generated fatal-within-severe is not calibrated (+0.055/+0.072/+0.062/-0.007/+0.001 vs observed +0.10 to +0.14; observed p=0.13). 'And in fatal cases' only restates severity.",
+      "The WHO-grade association is mostly between donors. Within donors the slope is positive in 5/5 but p<0.05 in only 1/5 (draw 3).",
+      "The test choice matters. A permutation test of the donor mean difference gives severity p 0.016-0.13 (2/5), against Mann-Whitney U 5/5, because the distributions are skewed. The acute-window analysis is the more robust statement.",
+      "The draws are not independent replications: per-sample fractions correlate 0.72-0.89 across draws because of shared sampler noise, so '5/5' overstates the evidence.",
+      "There is no information beyond bulk. Likelihood-ratio p for adding the fraction to bulk ISG10 + HLA2 is 0.77-0.95 on donor means and 0.03-0.29 on the acute first sample (1/5).",
+      "A minor number mismatch: I get a pooled within-sample observed/expected ratio of 0.88-0.95, not 0.75-0.87. The conclusion (no positive coupling) is unchanged.",
+      "calibration.csv labels its severity rows '(COVID+)' but they include the 10 paired recovery samples (49 vs 62 samples). The discovery agent correctly recalibrated on COVID+ only.",
+      "Not verified here: the embedding cluster (Mono r2.0 c12), the shared marker lists (s13), the BH q-values over the discovery's 17-rule family (raw p-values match exactly), and novelty."
+     ],
+     "reasoning": "Verdict: qualified. The effect reproduces exactly and survives almost every confounder control, but three things limit it: it is an acute-phase effect hidden by the bulk-only design, the HLA-II-low half is weakly calibrated per sample, and the fatal association is not separate from severity.\n\n**Reproduction.** I recomputed the rule from the raw marker counts and labels with my own thresholding, thinning and statistics. The bulk-only per-draw effects, observed truth and calibration all reproduce to the stated decimals. My calibration differs only through the thinning seed. The trust gate passes in all 5 draws (COVID+ ratio 0.72-1.20, Spearman 0.81-0.85, severity sign 5/5), so all five draws carry the COVID, severity and WHO-grade results. No draw carries fatal within severe: draw 3 has the wrong sign, draw 4 is about 0, and draws 0-2 give about half the observed size.\n\n**Controls the effect survives:**\n- age, sex and wave (wave 1 only: 5/5);\n- the sex-flag sample and training-donor samples (5/5);\n- repeated measures (donor means, first sample per donor: 5/5);\n- label confidence (≥0.95 only: 5/5);\n- library depth (thinned to 500 UMIs: 4/5);\n- granulocyte or platelet contamination (absent in generated state cells);\n- timing. Adjusting for days since swab strengthens the effect, because severe bulk-only samples were taken later and ISG fades with time.\n\n**The one control that removes it.** The generated CD14 share is twice as high in severe samples. This is uncalibrated: the observed share does not change with severity, and the generated share tracks bulk neutrophil genes. Adjusting for it removes the donor-level severity effect. However, the same adjustment also removes the severity association of the real bulk ISG10 score, which no generator artefact can cause. So the share is a proxy for the bulk severity signal, and adjusting for it is over-adjustment, not a confounder control. In the acute first sample per donor, with days and share adjusted, the effect still holds in 5/5 draws.\n\n**Qualifications the claim needs:**\n1. **Acute-phase effect.** In bulk-only data the severity effect is confined to ≤14 days. A design imbalance hides it: the unadjusted sample-level effect is 0/5.\n2. **Weak HLA calibration.** The HLA-II-low half of the definition calibrates poorly per sample (Spearman 0.09-0.48) and is over-produced. The state's per-sample calibration and its bulk-only variation (rho 0.83-0.92 with bulk ISG10) come mainly from the ISG part.\n3. **Fatal is severity restated.** The fatal association disappears after adjusting for peak severity, in both generated and observed cells.\n4. **WHO grade is mostly between donors.** The within-donor slope is significant in only 1/5 draws.\n5. **Nothing beyond bulk.** The fraction adds no information beyond bulk ISG/HLA, and the draws share sampler noise.\n\n**Paired observed cells.** They agree on direction and size for COVID, severity and WHO grade, and the observed effect is not changed by CD14-share adjustment.\n\n**Scope.** CPU only; no GPU work started. All files were written under my verify directory. I made no archive or maintenance changes; whether anything goes to the paper archive is for the coordinator.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/v00_explore.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/v01_fracs.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/v02_assoc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/v03_truth_calib.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/tables/v03_calib.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/tables/v04_models.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/tables/v04_windows.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_recompute/logs/v00_calibration_rows.log"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a2b9e68b46081b645",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt the rule from cells.parquet and markers.npz with my own code and my own thinning seed (777001). Threshold: 23.09 CP10k, from 9,735 COVID- observed CD14 cells.\n\nLEVELS. Observed native 14.3%; observed thinned 11.8-14.3%; paired_gen 10.8-15.6%; bulk-only 9.3-13.7%.\n\nBULK-ONLY RESULTS (match the candidate exactly).\n- Severity: +0.061/+0.071/+0.083/+0.096/+0.074 for d0-d4 (p 0.017/0.012/0.020/0.0034/0.011).\n- Fatal: +0.145 to +0.190 (p 0.0005-0.0056).\n- WHO grade: +0.046 to +0.066 per grade (p 0.0001-0.010).\n- COVID: +0.092 to +0.119.\n\nCALIBRATION, PER DRAW. Uses draw j's test samples, restricted to COVID+ for severity, fatal and WHO.\n- Ratio 0.72/0.93/1.21/0.78/0.80; Spearman 0.81/0.79/0.84/0.81/0.83.\n- Severity, generated vs observed: +0.146/+0.149/+0.201/+0.147/+0.111 vs +0.150/+0.148/+0.153/+0.202/+0.151.\n- Sign agrees in 5/5 draws for COVID, severity, fatal and WHO, so all 5 draws pass the gate for these contrasts.\n- Wave-1 test samples only: severity sign agrees 5/5; ratio 0.88-1.37.\n- Fatal within severe: generated +0.055/+0.072/+0.062/-0.007/+0.001 (p 0.64-1.0) vs observed +0.08 to +0.15. No draw supports this contrast.\n- Cut-off grid (ISG k = 3/4/5 x HLA threshold at p10/p25/p50 = 9 settings): truth is significant in all 9; calibration sign agrees 5/5; bulk-only severity p<0.05 in 4-5 of 5 draws.\n\nCOMPONENTS.\n- The composite tracks the ISG+ share closely: Spearman 0.89-0.96 in every set.\n- The HLA-low share calibrates poorly: Spearman (COVID+) 0.17/0.50/0.49/0.37/0.45; ratio 1.26-1.44.\n- The HLA-low share among ISG+ cells is not calibrated: Spearman -0.01/0.31/0.11/0.14/0.39.\n\nOBSERVED NATIVE DECOMPOSITION (samples with at least 5 cells in the conditioning group).\n- HLA-low share, severe vs mild: +0.073 among ISG+ cells (p=0.29) vs +0.215 among ISG- cells (p=0.003).\n- HLA-low share per WHO grade: +0.088 among ISG+ vs +0.105 among ISG-.\n- The ISG+ minus ISG- gap does not track severity (p=0.26) or WHO (p=0.68).\n- Excess over independence (frac - f_isg*f_low) does not track severity (p=0.57) or WHO (p=0.27).\n- Wave 1 only: HLA-low among ISG+ cells +0.04 (p=0.84), while the ISG+ share rises +0.40 (p=0.013).\n- The candidate's 0.42 vs 0.27 (p=0.026) appears only when samples with 1-4 ISG+ cells are kept. With the same setting, ISG- cells show +0.208 (p=0.008).\n- Donor-level severity AUC, composite vs product of its two marginals: observed 0.839 vs 0.835; bulk-only 0.68-0.73 vs 0.65-0.72. The HLA-low share alone reaches 0.74-0.80 in draws 1-4.\n\nBULK-ONLY DONOR SUBSETS.\n- 47 donors with no single cells at all: severity +0.032 to +0.090, p 0.12-0.53 (0/5 draws significant); fatal p 0.043-0.21 (1/5); WHO p<0.05 in 4/5. Real bulk HLA2 still separates severity in these donors (p=0.002).\n- 29 donors whose other visits have paired single cells: severity p 0.0035-0.043 (5/5).\n\nWITHIN-DONOR WHO SLOPE.\n- Generated fraction: +0.031 to +0.054, p 0.007-0.11 (1/5 draws below 0.05).\n- Real bulk ISG10-HLA2: p=0.0013.\n- Observed native fraction: +0.065, p=0.031.\n\nBEYOND BULK.\n- Real bulk ISG10-HLA2, same bulk-only donors: severity p=0.0066, fatal p=0.0004, WHO p<1e-4. HLA2 alone gives severity p=0.0001.\n- LR p for adding the generated fraction:\n  - to an ISG10+HLA2 baseline: severity 0.46-0.99, fatal 0.14-0.87;\n  - to the 4-score baseline: severity 0.66-0.89, fatal 0.19-0.66.\n- The observed fraction does add to the 2-score baseline for fatal (p=0.009; only 8 fatal donors), but not to the 4-score baseline (p=0.20).\n\nTRANSFER CHECKS.\n- Severe/critical bulk-only vs paired samples: bulk ISG10 7.41 vs 8.29 (p=0.041); 13.4 vs 9.3 days since swab (p=0.043); 88% vs 36% wave 1.\n- CD14 library medians, bulk-only vs paired_gen: within 8% in every draw.\n\nCD163. Share of rule-positive cells that are CD163+: observed 39%; generated 21-25% in d0-2 and 42-53% in d3-4.",
+     "issues": [
+      "The 'subset' / 'sub-state' reading is contradicted by the observed cells. The HLA-II decline with severity is global in CD14 monocytes and is larger in ISG- cells (+0.215, p=0.003) than in ISG+ cells (+0.073, p=0.29). The excess over independence tracks neither severity (p=0.57) nor WHO (p=0.27). The composite's severity AUC equals that of the product of its marginals (0.839 vs 0.835). The beyond_bulk sentence 'they place the bulk ISG-high/HLA-DR-low severity signal in one CD14 sub-state' is therefore not supported.",
+      "The quoted 'HLA-low share among ISG-high cells 0.42 severe vs 0.27 mild (p=0.026)' depends on keeping samples with only 1-4 ISG+ cells. With at least 5 such cells it is +0.073 (p=0.29); in wave 1 it is +0.04 (p=0.84). ISG- cells show the same or a larger shift in every setting.",
+      "The generator does not calibrate the joint state. Spearman for the HLA-low share among ISG+ cells is -0.01 to 0.39 per draw. The HLA-low share is over-produced 1.26-1.44x with Spearman 0.17-0.50. The composite passes calibration mainly because it is an ISG readout (rho 0.89-0.96 with the ISG+ share) and because the level errors cancel; the candidate already acknowledges the cancellation.",
+      "In the 47 bulk-only donors with no single cells at all, severity is not significant in any draw (p 0.12-0.53; effects +0.03 to +0.09) and fatal is significant in 1/5. The significant bulk-only severity and fatal results come from the 29 donors whose other visits are in the paired observed set. In those 47 new donors, the real bulk HLA2 does separate severity (p=0.002).",
+      "The WHO-grade slope is mostly a between-donor effect. Within donors, the generated slope reaches p<0.05 in only 1/5 draws (p 0.007-0.11), while the real bulk ISG10-HLA2 tracks WHO within donors (p=0.0013).",
+      "Fatal within severe/critical fails the paired calibration: the generated contrast is about 0 (d3 -0.007, d4 +0.001; generated p 0.64-1.0 in all draws). The bulk-only +0.144 to +0.188 is not reportable in any draw. The candidate already says so.",
+      "Calibration transfer is imperfect. Bulk-only severe samples are later (13.4 vs 9.3 days since swab, p=0.043), have lower bulk ISG10 (7.41 vs 8.29, p=0.041) and are 88% wave 1, against 36% wave 1 among paired severe samples. Calibration on wave-1 test samples alone still passes (same sign 5/5), but with few donors (5-6 severe).",
+      "Novelty is low, because the components are already reported for this very cohort.\n- Stephenson 2025 Fig 5B (p.11): IFN-stimulated CD14 monocytes are a larger share of monocytes in severe/critical than in mild/moderate (visual reading: median about 0.43 vs 0.05).\n- Stephenson 2025 Fig 5C protein panel (p.11): IFN-stimulated CD14 monocytes show lower surface HLA-DR protein (AB-HLA-DRA) than classical CD14 monocytes (visual reading).\n- Stephenson 2025 Fig 3C (p.5): HLA class II genes are down in total monocytes with severity.\n- Stephenson 2025 p.8: severe disease has a higher early IFN response and a steeper HLA class II decline over time.\n- Gisby 2022 (the bulk paper for this cohort) Fig 3c (p.6): bulk HLA-DRA falls with WHO severity (1% FDR in both waves).\n- Gisby 2022 pp.7-8: SIGLEC-1, one of the rule's 10 ISGs, rises on CD14+ monocytes earlier and more strongly in severe disease.",
+      "The 'differs from the non-ESKD mild-ISG pattern' framing is contradicted.\n- Stephenson 2025 p.12: 'We did not observe any COVID-19-associated effects that were specific to ESKD'.\n- Gisby 2022 p.8 says the same.\n- Within samples, ISG+ cells are less often HLA-low than ISG- cells (negative coupling), as in Schulte-Schrepping 2020 p.1427 (ISG+ monocytes are HLA-DRhi).\n- The global HLA-DR loss with severity matches COMBAT 2022 p.922.",
+      "Possible treatment confound in the marker description. The listed profile (HLA-DRB5/CD74 down, AP-1/IEG genes down, CD163+) overlaps the glucocorticoid-induced 'dex. monos' of Stephenson 2025 (p.10, Fig 5C), which appear only in the 2021 (wave-2) cohort. These marker contrasts compare cells across samples, so sample-level effects such as dexamethasone or handling enter them. The composite still holds in wave-1 observed samples (+0.155, p=0.0096).",
+      "The fraction adds nothing beyond the bulk (confirmed, and it is weaker than the bulk). In the same bulk-only donors, a two-gene-set bulk score (ISG10-HLA2) gives severity p=0.0066, fatal p=0.0004 and WHO p<1e-4, and HLA2 alone gives severity p=0.0001. The generated fraction's AUC (0.68-0.73) is below the 4-score bulk baseline (0.83-0.84).",
+      "Minor inaccuracies in the claim text:\n- 'About half CD163+': rule cells are 39% CD163+ in observed data and 21-25% in draws 0-2.\n- '143 COVID+ samples' for the WHO slope: 127-141 per draw after the 20-cell minimum.\n- '19 COVID- donors': 13-19 per draw.\n- The cluster counterpart Mono r2.0 c12 passes the gate only in draws 0, 3 and 4. Its stability Jaccard is 0.25, and only 17% of rule-positive cells fall in it.",
+      "The draws share sampler noise (seed 20260927), so the 5/5 draw agreement is not independent replication. The candidate already acknowledges this."
+     ],
+     "reasoning": "My own recomputation, with a different thinning seed, reproduces every quoted bulk-only, calibration and truth number. The fraction-level associations are real. The CD14 ISG+ HLA-II-low fraction is higher in COVID+, in severe/critical, at higher WHO grade and in fatal cases. This holds in the paired observed cells (also in wave 1 alone), passes the per-draw paired calibration in all 5 draws for those four contrasts, and survives a 3x3 cut-off grid. So it is not refuted.\n\nIt is not confirmed either, because the observed data contradict the biological reading the claim attaches to it.\n\n1. No distinct state. In observed cells the HLA-II-low shift with severity is global (larger in ISG- CD14 cells). The overlap fraction equals the product of an ISG+ expansion and a global HLA-II decline: the excess over independence and the ISG+ minus ISG- gap track neither severity nor WHO. In wave 1, ISG+ cells are no more often HLA-low in severe than in mild donors. The generator reproduces the ISG axis well but not the joint state (Spearman of about 0 for the HLA status of ISG+ cells). The composite passes calibration mostly as an ISG readout, plus level errors that cancel.\n2. Bulk-only evidence is narrower than stated. The severity and fatal effects are not significant in the 47 bulk-only donors that never had single cells. WHO tracking within patients is weak in the generated fraction, while the real bulk shows it clearly.\n3. Nothing beyond bulk. Every association is equal or stronger in a simple bulk ISG10-HLA2 or HLA2 score. The supposed added value, 'attribution to one CD14 sub-state', is contradicted by point 1.\n4. Little novelty. The same cohort's papers already show the components: IFN-stimulated CD14 monocytes expanded in severe/critical disease (Stephenson 2025 Fig 5B, p.11), lower HLA-DR protein on IFN-stimulated CD14 monocytes (Fig 5C, p.11), HLA class II loss with severity (Fig 3C p.5; p.8), and bulk HLA-DRA falling with WHO grade plus early monocyte SIGLEC-1/IFN in severe disease (Gisby 2022 pp.6-8). Both papers state that the COVID patterns are not ESKD-specific.\n\nDefensible restatement: in the paired calibration, the generated CD14 compartment reproduces the bulk's ISG-up / HLA-II-down severity axis in all 5 draws (COVID, severity, fatal and WHO; not fatal within severe), and their overlap fraction tracks severity, as the cohort papers already report. It is not a coupled IFN+ HLA-II-low state, and it adds no information beyond the bulk.\n\nNo GPU work was run. Everything was written under the verify dir only. This is a verification with no change to paper evidence, so there is no archive delta.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/v05_collect_quoted.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_levels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_libs.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v01_fracs.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v02_decomposition_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v02_component_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v02_bulk_score_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v02_increment.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v02_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v03_cutoff_grid_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v03_conditional_shares.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v03_components_by_donor_subset.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v03_component_spearman.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v03_cd163.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v04_observed_by_wave.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v04_within_donor_who.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v04_auc_composite_vs_marginals.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v05_literature_quotes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v05_candidate_s04_mono_r2_c12.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v05_candidate_s07a_rule_vs_cluster.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v05_candidate_s07b_components.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/tables/v05_candidate_s09_coupling_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/logs/v01_recompute.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/logs/v02_decomp_bulk.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/logs/v03_robustness.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-1_calib/logs/v04_wave_within.log"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-CLIN-2",
+   "lens": "imp_clinical",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 9,
+    "theme": "plasma cells",
+    "synthesis_strength": "moderate for direction in wave 1 and between donors (draws 1-4); nothing beyond bulk",
+    "reportable_statement": "Generated plasma cells (plasmablasts) of bulk-only COVID+ patients rise with WHO severity at sampling (+0.51, +0.62, +0.44, +0.46, +0.40 logit per step), mostly between patients, and with peak severity within wave 1 (+0.79 to +1.42), as in observed wave-1 cells. The direction is carried by draws 1-4 and the size by draws 3-4. A plasma-cell bulk marker score carries the same association and tracks patients better.",
+    "carrying_draws": {
+     "WHO, COVID+": "direction 1-4 (gates FAIL, DIR, DIR, PASS, PASS); size 3-4",
+     "wave-1 WHO / peak": "1,2 PASS; 3,4 DIR; 0 FAIL",
+     "peak severity, all COVID+": "none (no observed contrast)"
+    },
+    "honesty": {
+     "calibration": "calibration.csv COVID row: ratio 0.23/0.66/1.21/0.61/0.67, Spearman 0.20-0.43 (gates FAIL, PASS, FAIL, DIR, PASS). WHO slope on COVID+ tests: generated +0.48/+0.71/+0.51/+0.33/+0.40 vs observed +0.52 to +0.55; generated-minus-observed |diff| <= 0.19 (p >= 0.36). Per-sample Spearman 0.27-0.45; in wave 2 0.06-0.39. Draw 0 fails on level (0.21x; training donors plasma-poor).",
+     "replication": "5/5 positive; survives wave, training donors, depth, label confidence, a label-free marker rule, LDG score, ethnicity/diabetes and leave-one-donor-out. Draw 3 loses significance at donor level (p 0.088-0.11).",
+     "observed": "Observed WHO +0.50 (p 0.007; 113 samples, 35 donors); wave 1 +0.69 (p 0.003); wave 2 +0.16 (p 0.56; bulk plasma score also null there). Wave-1 peak +1.27 (p 0.006), +0.81 (p 0.12) with cell number.",
+     "beyond_bulk": "None. A plasma-gene bulk score tracks observed plasma cells better (Spearman 0.63-0.71 vs 0.20-0.45); given it, observed plasma cells have no residual WHO effect (+0.11 SD, p 0.24) while draws 0, 1, 4 do (+0.23 to +0.47; direct test significant in draw 1, p 0.042), so their tiny p-values are partly excess. A ridge model on bulk genes gives the same effects."
+    },
+    "verdict_reasons": [
+     "'Draws 1-4 calibrated' overstates: on the WHO contrast only draws 3-4 pass; draws 1-2 are direction-only.",
+     "Observed truth is wave-1 and between-donor only; it weakens with cell number and LDG adjustment.",
+     "Wave-2 bulk-only samples get slopes (+0.64 to +0.81) that nothing supports.",
+     "Peak severity over all COVID+ is uncalibrated (observed +0.33, p 0.41).",
+     "calibration.csv's severity row mixes in paired recovery samples (trust-layer bug).",
+     "Nothing beyond bulk."
+    ],
+    "refuted_subclaims": [
+     "draws 1-4 calibrated",
+     "beyond-bulk WHO component in draws 0, 1, 4"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 3A (B-ASC higher at severe/critical sampling, BH p 0.00031; text PDF p.6, legend p.7); Gisby 2022 module tL (PDF p.5) and CD38hi plasmablasts at day 14 in severe/critical (PDF p.7); also Bernardes 2020, Wilk 2021, COMBAT 2022. Stephenson 2021 notes less expansion in critical disease.",
+    "next_steps": [
+     "Restrict to wave 1 and report draws 3-4 sizes.",
+     "Fix plasma-cell under-generation in draw 0.",
+     "Report the bulk-score-adjusted generated residual; only a residual that the truth also has can be beyond bulk."
+    ],
+    "key_sources": [
+     "verify/IMP-CLIN-2_recompute/v03_assoc.csv",
+     "verify/IMP-CLIN-2_recompute/v04_contrast_gates.csv",
+     "verify/IMP-CLIN-2_recompute/v05_observed.csv",
+     "verify/IMP-CLIN-2_recompute/v07_beyond_bulk_paired.csv",
+     "verify/IMP-CLIN-2_recompute/v07_bulk_predictor_baseline.csv",
+     "verify/IMP-CLIN-2_calib/v05_gates.csv",
+     "verify/IMP-CLIN-2_calib/v04_direct_tests.parquet",
+     "verify/IMP-CLIN-2_calib/v03_bulk_beyond.parquet"
+    ]
+   },
+   "finding": {
+    "id": "IMP-CLIN-2",
+    "cohort": "imperial_eskd",
+    "claim": "Generated plasma cells (plasmablasts) increase with WHO severity at the sample between COVID+ patients, by +0.40 to +0.62 logit per WHO step (5/5 draws; draws 1–4 calibrated). Within wave 1 they also increase with peak severity (+0.79 to +1.42). Observed cells agree, but a plasma-cell bulk marker score already shows the association and tracks plasma cells better.",
+    "kind": "composition",
+    "feature": "l2 | Plasma cell (identical to state | Plasmablast (label); supporting: state | Plasmablast (markers))",
+    "comparison": "WHO severity at sample (ordinal 1–4) among COVID+, between donors. Peak severity (severe/critical vs mild/moderate) among wave-1 COVID+. Also COVID+ vs COVID−.",
+    "effect_bulkonly": "WHO slope, logit per step, draws 0–4: +0.51, +0.62, +0.44, +0.46, +0.40. The between-donor part is +0.45 to +0.68; the within-donor part is not significant. Mean generated fraction 0.4%, 1.6%, 2.3%, 1.2%, 1.1% (draw 0 makes plasma cells at 0.2× observed). Peak severity, all COVID+: +0.89 to +1.42 (severe/mild fraction 0.0061/0.0023 to 0.0323/0.0141, 2.3–3.3×). Peak severity, wave 1: +0.79, +1.42, +1.05, +0.88, +0.90. COVID+ vs COVID−: +0.93 to +1.74.",
+    "stats": "Model: MixedLM logit ~ who + age/10 + male + d + d² + (1|donor), with d = (days since positive swab − 12)/10. Data: n = 143 COVID+ samples from 58 donors. Per-draw p: 5.3e-11, 1.3e-9, 8.2e-4, 2.5e-4, 1.0e-6; BH q: 8.8e-10, 3.3e-8, 2.6e-3, 1.6e-3, 5.7e-6. Wave-1 peak severity (126 samples, 48 donors): q 5e-4, 3e-8, 2e-3, 0.02, 3e-6. All of these hold: S1–S5, ethnicity + diabetes adjustment, and LDG-score adjustment (+0.19 to +0.51, all p ≤ 0.04). In the 45 no-single-cell donors (S6) it holds in 4/5 draws (p ≤ 0.01; draw 3 p 0.1).",
+    "draws_same_sign": "5/5 (WHO, wave-1 peak, COVID); the carrying draws are 1–4. Shared seed noise explains about 2.9% of cross-draw agreement.",
+    "paired_calibration": "calibration.csv, COVID+ vs COVID− row: ratio 0.23/0.66/1.21/0.61/0.67; Spearman 0.28/0.34/0.20/0.29/0.43; same sign 5/5 (gen +0.002 to +0.013 vs obs +0.009 to +0.012); gates FAIL, PASS, FAIL, DIR, PASS. Severe vs mild row: same sign 5/5 (gen +0.002 to +0.012 vs obs +0.002 to +0.008); gates FAIL, DIR, FAIL, DIR, PASS. Recomputed for WHO at sample, donor-aware: gates FAIL (level 0.21), DIR, DIR, PASS, PASS (gen +0.33 to +0.71 vs obs +0.52 to +0.55). Wave-1 peak: FAIL, PASS, PASS, DIR, DIR. Peak severity on all paired test samples gives a NULL gate because the observed contrast there is diluted by wave 2: +0.33, p 0.41.",
+    "paired_observed_concordance": "Yes. Observed WHO slope +0.50 per step (p 0.0073; 113 samples, 35 donors); with a wave covariate +0.49 (p 0.006); wave 1 only +0.69 (p 0.003). Observed peak severity in wave 1: +1.27 (p 0.0073; 4.9% vs 1.4%), but across all paired samples only +0.33 (p 0.41). Observed COVID+ vs COVID−: +1.03 (p 1.9e-4).",
+    "beyond_bulk": "No. The plasma-cell bulk score (JCHAIN, MZB1, TNFRSF17, TXNDC5, IGLL5 …) shows the same associations in bulk-only samples: WHO +0.25 SD (p 0.006), peak +0.58 SD (p 0.015), COVID +1.22 SD (p 4e-8). It tracks the observed plasma fraction far better than the generated cells (Spearman 0.60–0.70 vs 0.20–0.45). Observed plasma cells carry no WHO information beyond the bulk score (+0.09 SD, p 0.33). The generator's own beyond-bulk component (draws 0, 1, 4) is therefore not supported by the truth. The full-bulk leave-one-donor-out AUC for peak severity is 0.743 without generated cells and 0.738–0.781 with them.",
+    "novelty": "Known. Stephenson 2025 PDF p. 6 (Fig. 3A: B-ASC proportion higher at severe/critical sampling, BH p 0.00031) and p. 3. Gisby 2022 Nat Commun (paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf): PDF p. 5, module tL rising with worse disease and associated with imputed plasma cells and immunoglobulin genes; PDF p. 7, CD38hi plasmablasts up at day 14 in severe/critical by flow.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s02_bulkonly_assoc.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s03_paired_calib.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s08_extra_models.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s08_extra_models.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s13_extra_gates.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s10_bulk_vs_gen.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_beyond_bulk_checks.log",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s15_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a5cb8b063a8c191a0",
+     "verdict": "qualified",
+     "recomputed": "I wrote my own code and started from the per-cell files. My plasma-cell counts from cells.parquet match composition.parquet exactly (difference 0). The marker-rule counts also match states.parquet. Plasma labels are confident: at least 95% of plasma-labelled cells have L2 confidence ≥ 0.9.\n\n**Bulk-only COVID+ (143 samples, 58 donors), same model as the claim**\n- WHO slope, draws 0–4: +0.511, +0.618, +0.439, +0.460, +0.404 logit per step; positive in 5/5.\n- statsmodels p: 4.6e-11, 1.3e-9, 8.2e-4, 2.5e-4, 1.0e-6.\n- My own REML: same lambda and coefficients, slightly smaller p (8.6e-12 … 4.6e-7). BH q across 50 features: 1.4e-10, 1.6e-8, 2.2e-3, 1.1e-3, 2.6e-6.\n- Mean generated plasma fraction: 0.42, 1.58, 2.34, 1.20, 1.10 %.\n- Between-donor part: +0.55, +0.68, +0.45, +0.49, +0.45. Within-donor part: +0.31, +0.47, +0.41, +0.39, +0.28 (p 0.094, 0.009, 0.098, 0.11, 0.065).\n- Peak severity, all COVID+: +0.89, +1.42, +0.99, +1.02, +0.97; severe/mild fraction ratio 2.3–3.3×.\n- Peak severity, wave 1: +0.79, +1.42, +1.05, +0.88, +0.90 (p 6e-5 … 4e-7).\n- COVID+ vs COVID−: +0.93, +1.74, +1.25, +1.42, +1.41.\n\n**Confounder controls: the effect stays positive in 5/5 draws under every control**\n- Holds:\n  - adding wave; wave 1 only;\n  - dropping the sex_flag sample; dropping samples whose donor trained the draw (p ≤ 0.002);\n  - adjusting for generated library size (p ≤ 0.02) or bulk read depth;\n  - labels with confidence ≥ 0.9; the label-free marker rule (+0.26 to +0.44, p ≤ 0.009);\n  - days since swab as a spline or as bins; days from symptoms instead; ethnicity + diabetes;\n  - a bulk LDG/neutrophil score (+0.30 to +0.49, p ≤ 0.021); dropping critical samples;\n  - leave-one-donor-out (minimum +0.37, maximum p 0.0056);\n  - all of these together, without sex_flag and trained-donor samples: +0.28 to +0.45, p 2e-6 to 0.046.\n- Draw 3 is the only draw that loses significance:\n  - one sample per donor: p 0.088;\n  - donor means: p 0.11;\n  - donors with no single cells: p 0.097.\n\n**Trust layer**\n- calibration.csv rows reproduce exactly:\n  - ratio 0.23, 0.66, 1.21, 0.61, 0.67; Spearman 0.28, 0.34, 0.195, 0.29, 0.43;\n  - COVID+ vs COVID− gates: FAIL, PASS, FAIL, DIR, PASS.\n- The official severe-vs-mild row includes the 10 paired recovery samples, despite its \"(COVID+)\" label. On COVID+ only, the plasma-cell gates become FAIL, PASS, FAIL, DIR, PASS (official: FAIL, DIR, FAIL, DIR, PASS).\n- Level on COVID+ test samples: ratio 0.21, 0.69, 1.18, 0.65, 0.68; Spearman 0.35, 0.28, 0.27, 0.36, 0.45.\n- WHO slope on the paired test samples, generated vs observed, same model:\n  - generated +0.48, +0.71, +0.51, +0.33, +0.40 vs observed +0.52, +0.53, +0.55, +0.55, +0.53;\n  - gates FAIL, DIR, DIR, PASS, PASS.\n- Restricted to wave-1 COVID+: WHO gates FAIL, PASS, PASS, DIR, DIR; the level ratio for draws 3 and 4 is 0.49 and 0.50.\n- Wave-1 peak severity gates: FAIL, PASS, PASS, DIR, DIR. Peak severity on all paired samples: observed +0.33 (p 0.41), so there is no observed contrast to gate against.\n\n**Observed truth (paired COVID+, 113 samples, 35 donors)**\n- WHO slope +0.50 (p 0.007).\n- Holds or weakens:\n  - adding wave: +0.49 (p 0.006);\n  - wave 1 only: +0.69 (p 0.001);\n  - wave 2 only: +0.16 (p 0.56);\n  - adding log cell number: +0.46 (p 0.015);\n  - adding the LDG score: +0.34 (p 0.071);\n  - between-donor part +0.68 (p 0.02); within-donor part +0.38 (p 0.12).\n- Adding the bulk plasma score leaves +0.17 (p 0.23).\n- Wave-1 peak severity +1.27 (p 0.006; 4.9 % vs 1.35 %).\n  - With log cell number: +0.81 (p 0.12).\n  - With the bulk plasma score: +0.17 (p 0.68).\n- COVID+ vs COVID−: +1.03 (p 2e-4); with the bulk plasma score +0.28 (p 0.22).\n\n**Bulk baseline**\n- My plasma-specific bulk score (JCHAIN, MZB1, TNFRSF17, TXNDC5, IGLL5) in bulk-only samples:\n  - WHO +0.24 SD (p 0.014); COVID +1.15 SD (p 1e-6);\n  - peak severity, all COVID+: +0.43 SD (p 0.085); wave 1: +0.38 SD (p 0.17).\n- A ridge model on 2,000 bulk genes was trained on the observed paired plasma cells (leave-one-donor-out Spearman 0.58).\n  - Applied to bulk-only samples: WHO +0.44 (p 3e-6), wave-1 peak +1.01 (p 1e-4). These are the generated values.\n  - On the paired samples it reproduces the observed slopes: WHO +0.42, peak +1.44.\n- Tracking the observed plasma fraction (Spearman): bulk score 0.63–0.71 vs generated cells 0.20–0.45. The generated fraction adds nothing beyond the bulk score.\n- Generator's WHO effect beyond the bulk score, paired test samples:\n  - generated, draws 0–4: +0.34, +0.54, +0.23, +0.08, +0.23 (p 0.004, 1e-4, 0.21, 0.54, 0.026);\n  - observed: +0.14 to +0.21 (all n.s.).\n\n**Shared seed noise**\n- Slot-level co-occurrence estimator: 1.5 % of the cross-draw covariance (range 0.7–3.2 %), against the claimed 2.9 %.\n- The same slot is a plasma cell in two draws 10–20× more often than chance.",
+     "issues": [
+      "Calibration is overstated by 'draws 1–4 calibrated'. On the claim's own WHO restriction (COVID+), only draws 3 and 4 PASS. Draws 1 and 2 are direction-only because their COVID+ Spearman is 0.28 and 0.27, under 0.3. Draw 2 fails both official calibration.csv rows (Spearman 0.195). On a wave-1 restriction, which covers 88% of the bulk-only samples, the codes flip to FAIL/PASS/PASS/DIR/DIR, and the level ratio for draws 3–4 is 0.49 and 0.50, right at the cut-off. Per-sample tracking is weak in every draw (COVID+ Spearman 0.27–0.45).",
+      "Trust-layer bug: calibration.csv's 'severe/critical vs mild/moderate (COVID+)' contrast includes the 10 paired recovery samples (n 49 vs 62 and so on). The official severity gates the claim quotes (FAIL/DIR/FAIL/DIR/PASS) were therefore computed on the wrong population. On COVID+ only they are FAIL/PASS/FAIL/DIR/PASS. The bug affects the severity gate of every Imperial feature.",
+      "The observed truth agrees only in wave 1 and only between donors. Wave 2: +0.16 (p 0.56; 62 samples, 15 donors). Within-donor part: +0.38 (p 0.12). The observed WHO slope weakens to +0.34 (p 0.071) with the bulk LDG score and to +0.40–0.46 (p 0.015–0.022) with log observed cell number. The observed wave-1 peak-severity contrast drops from +1.27 to +0.81 (p 0.12) with cell number, so the target of the wave-1 peak gate is fragile.",
+      "Draw 3, one of the two PASS draws, loses significance at the donor level: one sample per donor p 0.088, donor means p 0.11, donors without single cells p 0.097. Draws 0, 1, 2 and 4 hold under every control.",
+      "Beyond bulk is confirmed as none. In addition, in draws 0, 1 and 4 the generator keeps a WHO effect after the bulk plasma score on the paired test samples (+0.34, +0.54, +0.23; p 0.004, 1e-4, 0.026), while the truth does not (+0.14 to +0.21, n.s.). Part of those draws' bulk-only slope is therefore excess the truth does not support; draw 1 overshoots on paired test samples (+0.71 generated vs +0.53 observed). A ridge model on bulk genes, trained on paired truth, gives the same bulk-only effects (WHO +0.44, wave-1 peak +1.01).",
+      "Whether a bulk marker score shows the peak-severity association depends on the genes. With a plasma-specific 5-gene score, bulk-only peak severity is n.s.: +0.43 SD (p 0.085) for all COVID+ and +0.38 SD (p 0.17) for wave 1. The claim's +0.58 SD (p 0.015) uses a score that includes proliferation genes (TYMS, UBE2C, MYBL2, CDC20). The WHO (+0.24 SD, p 0.014) and COVID (+1.15 SD) associations of the score reproduce.",
+      "Minor: the within-donor WHO parts are positive in 5/5 draws (+0.28 to +0.47) and nominally significant in draw 1 (p 0.009). They are 'not significant' only after BH correction (q 0.23–0.70).",
+      "Minor: draw 0's statsmodels p differs by optimizer: 5.3e-11 at the lbfgs fit, 4.6e-11 at the boundary optimum. lbfgs returns degenerate fits (llf inf) for draws 1–4. Shared-noise share by slot co-occurrence is 1.5% (0.7–3.2%), against the claimed 2.9%. Both are small."
+     ],
+     "reasoning": "**Reproduction.** I recounted plasma cells from cells.parquet and refit the claim's mixed model with my own REML implementation, cross-checked against statsmodels. The WHO slopes, peak-severity contrasts, COVID contrast, mean fractions and between-donor parts reproduce to the second decimal. The sign is positive in 5/5 draws, and q values are of the same order as the claim's.\n\n**Confounders.** No reasonable confounder control removes the effect. I tested:\n- age, sex, wave and wave-1-only;\n- the sex_flag sample and trained-donor samples;\n- generated per-cell library size and bulk read depth;\n- label confidence, plus a label-free marker rule;\n- flexible day models and days from symptoms;\n- ethnicity and diabetes; an LDG/neutrophil bulk score;\n- one sample per donor, donor means, leave-one-donor-out, and dropping critical samples;\n- all of these together.\n\nAll stay positive in 5/5 draws. Only draw 3 loses significance at the donor level. So the effect is not refuted.\n\n**Why qualified, not confirmed.**\n1. The trust layer is moderate. For the WHO contrast only draws 3–4 PASS, draws 1–2 are direction-only, and draw 0 fails at a level ratio of 0.21. Draw 2 fails the official rows, and per-sample Spearman is 0.27–0.45. Gate codes flip between the all-wave and wave-1 restrictions, and two wave-1 levels sit at the 0.5 boundary.\n2. The truth supports the association mainly in wave 1 and between donors. It weakens with cell number and LDG adjustment, and the wave-1 peak-severity target is not robust to cell number.\n3. Nothing is beyond bulk. A bulk score tracks observed plasma cells far better. A ridge model trained on paired truth reproduces the bulk-only severity effects. In draws 0, 1 and 4 the generator adds a WHO component beyond the bulk score that the truth does not support.\n4. calibration.csv's severity row mixes in recovery samples. This is a trust-layer bug to fix for all Imperial features.\n\n**Carrying draws.** The defensible headline is draws 3 and 4 PASS and draws 1 and 2 direction-only, for WHO in COVID+. The finding is known (Stephenson 2025 p. 6, B-ASC higher at severe/critical sampling; Gisby 2022 p. 5, module tL tied to imputed plasma cells and immunoglobulin genes, and p. 7, CD38hi plasmablasts up at day 14) and bulk-derivable.\n\nAll work ran on CPU only, inside a 6 GB systemd scope with CUDA disabled. I wrote only under my verify directory. I made no paper-archive update; the coordinator owns that.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_recompute/v08_shared_noise.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a207404e0326799fc",
+     "verdict": "qualified",
+     "recomputed": "All numbers are my own, recomputed with independent code (vcommon.py, v01–v09) using the same model: MixedLM REML, empirical logit, covariates who + age/10 + male + d + d², donor random intercept.\n\nBULK-ONLY WHO SLOPE (143 COVID+ samples, 58 donors), logit per step, draws 0–4:\n- All samples: +0.51 (p 4.6e-11), +0.62 (1.3e-9), +0.44 (8.2e-4), +0.46 (2.5e-4), +0.40 (1.0e-6).\n- Wave 1 only: +0.49, +0.62, +0.50, +0.44, +0.42.\n- Donor-level aggregation p: 3.6e-4, 1.1e-4, 0.031, 0.11, 1.2e-3.\n- Leave-one-donor-out, largest p: 1.9e-6, 4.8e-8, 0.0065, 0.0027, 1.6e-5.\n- S1 and S2 sensitivity sets: unchanged.\n- S6 (donors with no single cells): +0.49, +0.61, +0.51, +0.30 (p 0.10), +0.31.\n- Between-donor part: +0.55, +0.68, +0.45, +0.49, +0.45.\n- Within-donor part: +0.31 to +0.47, raw p 0.009–0.11. The claim's \"not significant\" uses BH q.\n\nPAIRED TEST CALIBRATION, WHO, all COVID+ test rows:\n- Generated +0.48, +0.71, +0.51, +0.33, +0.40 vs observed +0.52, +0.53, +0.55, +0.55, +0.53 (observed p 0.003–0.011).\n- Level ratio 0.21, 0.69, 1.18, 0.65, 0.68. Spearman 0.35, 0.28, 0.27, 0.36, 0.45.\n- Gates: FAIL, DIR, DIR, PASS, PASS.\n- Direct generated-minus-observed slope test on the same samples: −0.19 to +0.19, every p ≥ 0.36.\n\nWave 1 test rows:\n- Generated +0.57, +0.99, +0.57, +0.86, +0.53 vs observed +0.65, +0.69, +0.69, +0.71, +0.67.\n- Level 0.17, 0.53, 0.88, 0.49, 0.50. Spearman 0.59, 0.61, 0.47, 0.57, 0.66.\n- Gates: FAIL, PASS, PASS, DIR, DIR. Pooled generated/observed over draws 1–4: 1.07.\n\nWave 2 test rows:\n- Generated-vs-observed Spearman 0.15, 0.12, 0.06, 0.26, 0.39.\n- Draw 1 generates +0.55 (p 0.02) where observed is +0.10 (p 0.76): NULL_genfx.\n\nOBSERVED, ALL PAIRED SAMPLES (113 samples, 35 donors):\n- WHO: +0.50 (p 0.0073). With a wave covariate: +0.49 (p 0.0064).\n- Wave 1: +0.69 (p 0.0029). Wave 2: +0.16 (p 0.56; 62 samples, 15 donors).\n- Peak severity: all +0.33 (p 0.41), wave 1 +1.27 (p 0.0073), wave 2 +0.06 (p 0.89).\n- COVID+ vs COVID−: +1.03 (p 1.9e-4).\n\nPEAK-SEVERITY TEST GATES:\n- All COVID+: NULL_ok, NULL_genfx, NULL_ok, NULL_ok, NULL_genfx. Pooled generated/observed ratio 1.73 (draws 0–4) and 2.06 (draws 1–4).\n- Wave 1: FAIL, PASS, PASS, DIR, DIR.\n\ncalibration.csv CHECKS: gates verified from calibration.csv:\n- COVID+ vs COVID−: FAIL, PASS, FAIL, DIR, PASS.\n- Severe vs mild: FAIL, DIR, FAIL, DIR, PASS.\n- Supporting state \"Plasmablast (markers)\", WHO, compared through observed_thin: gates FAIL, DIR, DIR, DIR, PASS.\n\nBULK SCORE: 8 plasma-cell genes chosen before any testing (JCHAIN, MZB1, TNFRSF17, TXNDC5, DERL3, IGLL5, SDC1, PRDM1; FKBP11 dropped because it is zero in every sample).\n- WHO association: bulk-only +0.22 SD (p 0.019), bulk-only wave 1 +0.18 (p 0.076), paired +0.31 (p 0.011), paired wave 2 +0.12 (p 0.53).\n- Tracking of the observed plasma fraction (Spearman), bulk score vs generated:\n\n| Population | Bulk score | Generated |\n|---|---|---|\n| COVID+ test rows | 0.66–0.70 | 0.27–0.45 |\n| Wave 1 | 0.78–0.79 | 0.47–0.66 |\n| Wave 2 | 0.58–0.63 | 0.06–0.39 |\n\n- Observed WHO effect given the bulk score: +0.11 SD (p 0.24).\n- Generated WHO effect given the bulk score, test rows: +0.33 (p 0.005), +0.47 (7.5e-5), +0.14, +0.06, +0.23 (p 0.04).\n- Generated WHO effect given the bulk score, bulk-only: +0.43 (2e-8), +0.42 (4e-7), +0.17 (0.04), +0.18 (0.03), +0.28 (3e-4).\n- Direct test of the generated-minus-observed WHO effect beyond the bulk score: draw 1 +0.34 SD (p 0.042); the other draws p ≥ 0.14.\n- Bulk-score-predicted plasma slope in bulk-only: +0.27 per step (p 0.019); wave 1 +0.22 (p 0.076).\n- Calibration transfer: at wave-1 WHO 1, the bulk-only plasma score is higher than in paired samples (+0.12 vs −0.27 SD, p 0.044).\n\nDRAW BEHAVIOUR AND SHARED NOISE:\n- Draw 0 training donors carry few plasma cells (pooled 0.28% vs 1.16% in other donors).\n- Draw 1 trained on C65, a severe donor with 8.5% plasma cells.\n- Shared seed noise: slot lift 15.6× at the cell level, but the sample-level noise share is 0.029, which reproduces the claim's 2.9%.",
+     "issues": [
+      "Wave 2 is not calibrated, and the claim's WHO statement is not restricted to a wave. In paired samples the observed WHO association exists only in wave 1 (+0.69, p 0.003). In wave 2 it is null in the observed cells (+0.16, p 0.56) and also in the bulk plasma score (+0.12 SD, p 0.53), so the null is unlikely to be a single-cell processing artefact. In wave-2 test rows the generated cells barely track the observed plasma fraction (Spearman 0.06–0.39), and draw 1 creates a WHO effect the truth does not have (+0.55, p 0.02; NULL_genfx). The 17 bulk-only wave-2 samples get generated slopes of +0.64 to +0.81 that nothing supports. The finding should be stated for wave 1, which holds 126 of the 143 bulk-only COVID+ samples.",
+      "Per-sample tracking is weak. Generated vs observed plasma fraction has Spearman 0.27–0.45 on COVID+ test rows. A fixed bulk plasma-gene score tracks the observed fraction at 0.66–0.74, and does better in every draw and every wave. The generated values support only a group-level trend, not per-sample estimates.",
+      "The p-values overstate the evidence. Draws 0, 1 and 4 reach p 1e-6 to 5e-11 because of a generator component that the bulk plasma genes do not explain. Given the bulk score, the truth has no residual WHO effect (+0.11 SD, p 0.24), while the generator does: +0.33, +0.47 and +0.23 SD on test rows, and +0.43, +0.42 and +0.28 SD in bulk-only. The direct generated-minus-observed test of that residual is significant in draw 1 (+0.34 SD, p 0.042). The bulk score alone gives p 0.012–0.019 in bulk-only, and only 0.047–0.076 in wave 1. Draws 2 and 3 match the truth's mechanism best, but their donor-level p values are 0.031 and 0.11.",
+      "Peak severity over all COVID+ samples (+0.89 to +1.42) is not calibrated. Observed paired effect: +0.33 (p 0.41). Pooled generated/observed ratio: 1.7–2.1. Draws 1 and 4 are NULL_genfx. Only the wave-1 peak-severity contrast is calibrated (draws 1–4: PASS, PASS, DIR, DIR).",
+      "Calibration transfer is imperfect. Bulk-only wave-1 mild samples carry more plasma bulk signal than paired mild samples (p 0.044), so the bulk-implied WHO gradient is flatter in bulk-only (predicted +0.22 per step in wave 1). The generated slopes are not flattened (+0.42 to +0.62). This fits the extra generator component described in the p-value issue.",
+      "Draw 0 fails on level in every restriction (0.17–0.23× observed). Its training donors carry few plasma cells (pooled 0.28% vs 1.16%). It is correctly excluded.",
+      "\"Within-donor part not significant\" comes from BH q. The raw within-donor p is 0.009 in draw 1 and 0.07–0.11 in the other draws.",
+      "Citation detail: the BH p 0.00031 for Fig. 3A of Stephenson 2025 is in the figure legend on PDF p. 7; the text is on p. 6. The observed cells here are the Stephenson 2025 cells, so \"observed cells agree\" re-derives a published result. In non-ESKD COVID, Stephenson 2021 (PDF p. 8) reports that plasmablast and plasma-cell expansion is less evident in critical/severe than in mild/moderate disease, so a monotonic severity trend is not universal.",
+      "Draw agreement is not five independent replications at the cell level. A slot labelled plasma in one draw is 15.6× more likely to be plasma in another draw at the same slot. The effect on sample-level counts is small (2.9%), as the claim says."
+     ],
+     "reasoning": "I recomputed every quoted bulk-only, calibration and observed number with independent code and they match the claim. The core claim holds, but only partly.\n\n(1) Calibration. The paired TEST rows support the WHO direction in draws 1–4 (gates DIR, DIR, PASS, PASS) and in the wave-1 restriction (PASS, PASS, DIR, DIR). In no draw does the generated slope differ from the observed slope on the same samples (|diff| ≤ 0.19, p ≥ 0.36). Draw 0 fails on level. Per-sample tracking is weak (Spearman 0.27–0.45). In wave 2, tracking collapses (0.06–0.39) and draw 1 shows a severity effect that the truth does not.\n\n(2) Observed support. Observed paired cells reproduce the WHO association overall (+0.50, p 0.007) and in wave 1 (+0.69, p 0.003). They do not in wave 2 (+0.16, p 0.56), and the bulk plasma score is also null there. Peak severity is observed only in wave 1.\n\n(3) Biology and novelty. The finding is known. Stephenson 2025, the same single cells, reports more B-ASC at severe/critical sampling (PDF p. 6; Fig. 3A legend with BH p 0.00031 on PDF p. 7). Gisby 2022, the same PBMC bulk, reports the tL \"plasma cell module\" rising with worse disease and tied to imputed plasma cells and immunoglobulin genes (PDF p. 5, Fig. 4 on p. 7), and CD38hi plasmablasts up at day 14 in severe/critical by flow (PDF p. 7). Outside ESKD it agrees with Bernardes 2020 (PDF p. 2–3), Wilk 2021 (PDF p. 2) and COMBAT 2022 (PDF p. 10). Stephenson 2021 (PDF p. 8) says the expansion is less evident in critical/severe than in mild/moderate. Nothing here is new for this cohort.\n\n(4) Beyond bulk. The generated cells add nothing beyond the bulk. A plasma-gene bulk score carries the association and tracks the truth better in every draw and wave. The extra WHO signal in draws 0, 1 and 4 is not shared by the truth, which inflates their very small p values.\n\nNo layer contradicts the main direction, so the claim is not refuted. Support is partial on four counts: wave 2, peak severity over all COVID+ samples, per-sample tracking, and strength of evidence. So the verdict is qualified, not confirmed.\n\nHousekeeping: CPU only; nothing was written outside my verify directory. This subagent changed no paper assets and published no archive update; the archive policy step belongs to the coordinator.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v03_bulk_beyond.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v03_bulk_beyond.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v03_bulk_beyond.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v03_bulk_scores.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v04_direct_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v04_direct_tests.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v04_direct_tests.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v05_gates_markers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v05_gates_markers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v05_gates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v06_literature.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v06_literature_hits.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/stephenson2025_text.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/gisby2022_text.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v07_shared_noise.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v07_shared_noise.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v08_sensitivity.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v08_sensitivity.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v08_sensitivity.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v09_pooled.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-2_calib/v09_pooled.txt"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-PROG-5",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 10,
+    "theme": "plasma cells",
+    "synthesis_strength": "positive control for attribution in draws 1-3; magnitude ~10x too small; nothing beyond deconvolution",
+    "reportable_statement": "Ten cell-cycle genes among the top COVID+-up bulk genes (MKI67, TOP2A, BUB1, ASPM, RRM2, TYMS, MYBL2, CENPF, TPX2, KIF11) are attributed by draws 1-3 to the expansion of plasma cells (composition), as in observed cells. The generated plasma cells are real plasma cells but mostly not dividing, so they carry only 5-13% of the observed level of these genes. Standard deconvolution of the bulk gives the same attribution.",
+    "carrying_draws": {
+     "carrier = plasma (paired tests)": "1,2,3 (draw 0 1/10; draw 4 4/10, 9/10 in wave 1)",
+     "composition-driven": "29/30 gene x draw cells in draws 1-3",
+     "plasma share rise in bulk-only": "0-4 (p <= 6e-4)"
+    },
+    "honesty": {
+     "calibration": "Carrier agreement on paired tests 1, 10, 10, 10, 4 of 10, but a constant 'always plasma' guess also scores 10/10. calibration.csv l2 Plasma cell: ratio 0.23-1.21, Spearman 0.20-0.43, COVID sign 5/5, contrast 0.78-1.27x in draws 1-4. Within COVID+ the generated plasma share tracks observed at 0.27-0.45, a bulk plasma score at 0.67-0.71, NNLS at 0.76-0.79.",
+     "replication": "Plasma carrier in 8-10 of 10 genes in every bulk-only draw; draws 1-3 10/10 under every sensitivity subset.",
+     "observed": "Observed: plasma cells carry all 10 genes (share 0.70-0.97), composition-driven; observed plasma share +0.009 to +0.012 with COVID.",
+     "beyond_bulk": "None. Composition-only deconvolution (NNLS x atlas) gives 10/10 on bulk-only and 9-10/10 on held-out paired tests; Gisby 2022 made this attribution with CIBERSORTx. csSAM's 10/10 depends on the marker count (2-3/10 with 50-60 markers)."
+    },
+    "verdict_reasons": [
+     "Signal size fails calibration: generated cells carry 5-13% of the observed level and 3-15% of the COVID change (plasma cells cycle 0.17-0.44x).",
+     "Gene list hand-picked: four hallmark G2M/E2F genes in the top 30 (BRCA1, LMNB1, MXD3, STMN1) were left out; on 14 genes draws 1-3 score 12, 13, 12.",
+     "'All composition-driven in draws 1-3' is 29/30 (TPX2 expression-driven in draw 3).",
+     "Per-sample plasma share tracking is weak (Spearman 0.20-0.43).",
+     "The draws share the sampling seed; draws 1-3 agreement is not three replications."
+    ],
+    "refuted_subclaims": [
+     "all ten genes composition-driven in draws 1-3 (29/30)"
+    ],
+    "new_vs_cohort_papers": "Not new: Gisby 2022 Fig. 4a module tL 'Plasma cell module' with hub genes RRM2, FOXM1, MKI67, TPX2, BUB1 and its link to CIBERSORTx plasma proportion (PDF p.5, p.7); Stephenson 2025 PDF p.3-4 (dividing antibody-secreting B cells).",
+    "next_steps": [
+     "Use as a positive control for attribution.",
+     "Fix the under-proliferative plasma-cell output before claiming programme sizes.",
+     "Score every attribution against a constant prior and NNLS deconvolution."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-5_recompute/v08_summary.csv",
+     "verify/IMP-PROG-5_recompute/v02_agreement.csv",
+     "verify/IMP-PROG-5_recompute/v04_label_profile.csv",
+     "verify/IMP-PROG-5_recompute/v05b_cssam_sweep.csv",
+     "verify/IMP-PROG-5_calib/v03_identity.csv",
+     "verify/IMP-PROG-5_calib/v03_cc_level.csv",
+     "verify/IMP-PROG-5_calib/v04_deconv_summary.csv",
+     "verify/IMP-PROG-5_calib/v09_top30cc_agreement.csv",
+     "verify/IMP-PROG-5_calib/v08_within_group_tracking.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-5",
+    "cohort": "imperial_eskd",
+    "claim": "Ten cell-cycle genes are among the top COVID+-up bulk genes of bulk-only patients: MKI67, TOP2A, BUB1, ASPM, RRM2, TYMS, MYBL2, CENPF, TPX2 and KIF11. Draws 1-3 attribute all 10 to the expansion of plasma cells/plasmablasts (a composition effect), as the observed paired cells do. An abundance-only atlas would put 4 of them in T cells. But standard NNLS/csSAM deconvolution of the same bulk also gets 10/10, so the generated cells add nothing beyond deconvolution here.",
+    "kind": "composition",
+    "feature": "Coarse-lineage carrier of each bulk DE gene: the lineage with the largest donor-aware change in whole-sample CPM share, split into a composition part and an expression part.",
+    "comparison": "COVID+ vs COVID- (bulk-only 58 vs 19 donors).",
+    "effect_bulkonly": "Carrier = Plasma for 8, 10, 10, 10 and 8 of the 10 genes in draws 0-4; in draws 1-3 all are composition-driven. Bulk log2FC of these genes +1.07 to +1.57 (q <= 2e-5). Generated plasma-cell share difference (COVID+ minus COVID-) on paired tests: +0.0021 in draw 0 and +0.0083 to +0.0131 in draws 1-4.",
+    "stats": "Donor-aware decomposition of the whole-sample pseudobulk change (s04); bulk DE by donor-aware Welch with BH (1,118 panel genes with q < 0.05, mean CPM >= 10).",
+    "draws_same_sign": "In the bulk-only cells, Plasma is the carrier for 8-10 of 10 genes in all five draws. Only draws 1-3 are calibrated for it.",
+    "paired_calibration": "Carrier agreement with observed on paired tests: 1, 10, 10, 10 and 4 of 10 genes in draws 0-4. calibration.csv 'l2 | Plasma cell': ratio 0.23, 0.66, 1.21, 0.61, 0.67; Spearman 0.28, 0.34, 0.20, 0.29, 0.43; COVID sign 5/5; contrast 0.18x of observed in draw 0 and 0.78-1.27x in draws 1-4. Over all 30 COVID top-up genes, paired-test carrier agreement is 0.40, 0.77, 0.80, 0.70 and 0.57. Baselines: abundance-only reference 0.50-0.53; constant guess 0.60-0.63; csSAM 0.67-0.77.",
+    "paired_observed_concordance": "In all paired samples, Plasma carries all 10 genes (carrier share 0.70-0.97), driven by composition. Observed plasma-cell share rises by +0.009 to +0.012 with COVID.",
+    "beyond_bulk": "None compared with deconvolution. csSAM with NNLS fractions on the bulk-only bulk assigns all 10 genes to Plasma, and it agrees 10/10 on every test set. Only the naive abundance-only reference does worse (6/10). Over the 30 top-up genes, bulk-only generated carriers agree with observed 0.57-0.83, against 0.70 for csSAM.",
+    "novelty": "Consistent with Stephenson 2025 PDF p.3 (antibody-secreting B cells, including dividing ones, rise in COVID+). Also with Gisby 2022 PDF p.2 (cell-cycle pathways up in COVID+ bulk), p.5 (plasma-cell module tL) and p.7 (CD38-high plasmablasts). Standard deconvolution recovers the attribution, so it is not new.",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s04_attribution.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/attribution_genes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/attribution_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/attribution_beyond_ref.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_carriers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_carriers_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/deconv_carriers_bulkonly_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s12_cellcycle_attribution.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/cellcycle_attribution.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/cellcycle_attribution_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/de_bulk.parquet"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a927689e004e0c330",
+     "verdict": "qualified",
+     "recomputed": "All numbers come from my own code (vc.py, v00-v08), built from the raw compact files.\n\nBulk DE (bulk-only, donor-aware Welch over 58 vs 19 donors; BH over 12,059 genes with mean CPM >= 1):\n- 1,118 panel genes have q < 0.05 and mean CPM >= 10.\n- For the ten genes, log2FC is +1.07 to +1.57 and max q is 1.9e-5. They rank 1-19 among the top-up genes by t.\n- The DE survives every control I ran. The ten-gene diff stays between 0.93 and 1.77, with max p 5.2e-5. Controls: age+sex OLS; age+sex+ethnicity+diabetes+log raw library; wave-1 only; no sex_flag; one sample per donor (first or last visit); no dual-status donors; sample-level mixedlm; donor-level Mann-Whitney.\n- Raw bulk library size does not differ between groups (p = 0.67).\n- The DE replicates in the paired bulk (+1.21 to +1.75).\n\nCarriers (my donor-aware decomposition over 7 coarse lineages):\n- Plasma is the bulk-only carrier for 8, 10, 10, 10 and 8 of the 10 genes in draws 0-4. The off-Plasma calls are TYMS to T and KIF11 to B in draw 0, and MKI67 to T and TOP2A to Monocyte in draw 4.\n- Plasma carrier AND composition-driven: 4, 10, 10, 9 and 6. In draw 3, TPX2 is expression-driven, and the discovery's own attribution_genes.csv agrees.\n- In draws 1-3 the Plasma carriers stay at 10/10 under every control: no training donors, no sex_flag, wave 1 only, first sample per donor, a combined subset, age+sex adjusted, and a library-free cell-weighted version.\n- Observed (all paired): Plasma carries all 10 genes, with carrier share 0.70-0.97, all composition-driven.\n- Paired-test carrier agreement is 1, 10, 10, 10 and 4. The constant guess \"always Plasma\" also gets 10/10 in every draw.\n- Over the 30 top-up genes:\n  - paired-test agreement: 0.40, 0.77, 0.80, 0.70, 0.57 (constant guess 0.63, 0.60, 0.60, 0.60, 0.60)\n  - abundance-only reference: 6/10 Plasma on the ten genes (4 go to T: TOP2A, CENPF, TPX2, KIF11); agreement 0.50-0.53 on the 30 genes\n  - bulk-only carriers vs observed (all paired): 0.60, 0.83, 0.70, 0.77, 0.57\n\nPlasma-cell share:\n- The calibration.csv 'l2 | Plasma cell' rows reproduce exactly from cells.parquet:\n  - ratio 0.23, 0.66, 1.21, 0.61, 0.67\n  - Spearman 0.28, 0.34, 0.20, 0.29, 0.43\n  - gen/obs contrast 0.18, 0.89, 1.27, 0.87, 0.78\n- Restricted to wave-1 test samples, the contrast is 0.19, 0.72, 1.00, 0.68, 0.59.\n- The bulk-only effect, COVID+ minus COVID- (donor-aware), is significant in every draw:\n  - d0 +0.0029 (p = 6e-4)\n  - d1 +0.0127 (p = 5e-6)\n  - d2 +0.0159 (p = 6e-5)\n  - d3 +0.0096 (p = 2e-4)\n  - d4 +0.0081 (p = 4e-7)\n- Worst case across no-train-donor, no-sex_flag, wave-1, first-visit and label-confidence (>= 0.9 / >= 0.99) filters: diff >= +0.0072 in draws 1-4, Welch p <= 4e-3. The age+sex OLS p reaches 0.06 only in some draw 3 subsets.\n- Observed, all paired: +0.0117 (p = 0.005).\n\nLabel checks (embedding-free):\n- 95-99% of generated Plasma-labelled cells are marker plasmablasts (>= 3 of JCHAIN, MZB1, TNFRSF17, TXNDC5, PRDM1, IRF4, IGLL5).\n- The marker-defined plasmablast share rises in COVID+ in all draws.\n- Marker plasmablasts carry 0.53-1.28 of the MKI67/TOP2A/TYMS/CENPF change in draws 1-3, and 0.72-0.95 in observed cells.\n- Generated plasma cells cycle much less: MKI67 is detected in 5-17% of them, against 36% in observed plasma cells. As a result, the generated whole-sample log2FC of the ten genes is attenuated: d2 0.32-1.09 and d3 0.27-1.31, against bulk 1.07-1.57.\n\nDeconvolution and other bulk baselines:\n- csSAM (all-paired reference) gets 10/10 on every paired test only with 40 markers per lineage. With 20-30 markers it gets 6-10/10, and with 50-60 markers 2-3/10.\n- 4-9 of the 10 cell-cycle genes are themselves chosen as Plasma markers, so the attribution is partly circular.\n- A leakage-free csSAM using only draw j's 5 training-donor cells (25 markers) gets 10/10 on bulk-only and on every paired test.\n- Bulk co-expression of the ten genes with a bulk plasmablast score: Spearman 0.74-0.89. With T/NK/monocyte marker scores: |rho| <= 0.23.\n\nCalibration matched to the bulk-only population (COVID+ test samples with days since swab >= 7 or >= 10, or wave 1 and days >= 7):\n- Carriers agree 10/10 in draws 1-3 under every restriction.\n- The generated composition-driver count in draw 3 falls to 1/10 in the wave1 and days >= 7 subset, while observed stays at 10/10.\n- The draw 2 plasma contrast overshoots up to 2.8x.",
+     "issues": [
+      "Overstated sub-claim: 'in draws 1-3 all are composition-driven' is 29/30. In draw 3, TPX2 is expression-driven, both in my recompute and in the discovery's own attribution_genes.csv. In draw 3 the composition and expression parts are of similar size for several genes. The driver calibration is unstable there: carrier+driver agreement on all tests is 1.0, 1.0, 0.8 in d1-d3, and in wave-1 tests 0.7, 1.0, 0.1. In the wave1 and days >= 7 matched subset, only 1/10 generated genes stay composition-driven in d3, against 10/10 observed.",
+      "The deconvolution comparison is fragile. csSAM gets '10/10 on every test set' only with exactly 40 markers per lineage (the discovery's choice). With 50-60 markers, paired-test agreement falls to 2-3/10; with 20-30 markers it is 6-10/10. 4-9 of the 10 cell-cycle genes are selected as Plasma signature markers, so csSAM's Plasma attribution is partly circular. The 'nothing beyond bulk' conclusion still holds through other routes: a leakage-free 5-training-donor csSAM gets 10/10 everywhere at 25 markers, the bulk cell-cycle genes co-vary with a bulk plasmablast score (rho 0.74-0.89, |rho| <= 0.23 with T/NK/monocyte scores), and plasmablast genes (MZB1, IGLL5, POU2AF1, CD38) are themselves in the bulk top-30 up list.",
+      "For these ten genes, paired-test carrier agreement does not discriminate. The observed test carrier is Plasma for all 10 genes in every draw, so 'always Plasma' scores 10/10. The 10/10 in d1-d3 only shows that those draws do not fail the way d0 (1/10) and d4 (4/10) do.",
+      "Calibration of the plasma share is moderate at best. The contrast is reproduced (0.87-1.27x in d1-d3), but per-sample Spearman is weak: 0.34, 0.20 and 0.29 in d1-d3, and 0.27-0.36 within COVID+ only. Draw 0 fails (ratio 0.23, contrast 0.18x). Draw 4 passes on composition (0.78x, Spearman 0.43) but fails on carrier (4/10), because it over-generates proliferating T cells (ratio 2.0).",
+      "Magnitude is not reproduced. Generated plasmablasts are under-proliferative (MKI67 detected in 5-17% vs 36% observed). The bulk-only generated whole-sample log2FC of the ten genes is attenuated in d2 (0.32-1.09) and d3 (0.27-1.31) against the bulk 1.07-1.57, and is near zero in d0/d4, where carrier shares come from tiny totals.",
+      "The claim's 'effect_bulkonly' field quotes the paired-test plasma differences. The actual bulk-only differences (donor-aware) are +0.0029, +0.0127, +0.0159, +0.0096 and +0.0081 for d0-d4. All are significant and robust, so this is a reporting slip, not a failure.",
+      "The calibration population differs from the bulk-only population. Bulk-only COVID+ samples are later after the swab (median 12 vs 9 days, p = 0.001), 88% wave 1 vs 45%, and more often severe/critical at peak. Bulk-only COVID- samples have higher bulk cell-cycle expression than paired COVID- (+0.24 to +0.61 log2, p 0.002-0.24) without higher plasmablast markers. Matched calibrations keep the d1-d3 carriers at 10/10, but the draw 2 plasma contrast overshoots up to 2.8x when COVID+ test samples are restricted to days since swab >= 10.",
+      "Cross-draw agreement is not independent replication. All five draws used generation seeds 20260927 (part 0) and 840262485 (part 1) and Poisson seed 20260916. Per-sample plasma shares correlate across draws at Spearman 0.44-0.83, and still 0.22-0.78 after controlling for the bulk plasmablast score."
+     ],
+     "reasoning": "I recomputed every component from the raw files with my own code and did not import the discovery agent's scripts.\n\n1. The bulk DE reproduces exactly: 1,118 panel genes, the ten genes at +1.07 to +1.57 with q <= 1.9e-5. It survives age, sex, ethnicity, diabetes, raw library size, wave-1 restriction, sex_flag removal, one sample per donor, a sample-level mixed model and Mann-Whitney, and it replicates in the paired bulk.\n\n2. My independent donor-aware decomposition reproduces the per-draw carriers (Plasma 8, 10, 10, 10, 8), the paired-test carrier agreement (1, 10, 10, 10, 4), the observed all-paired carrier shares (0.70-0.97, composition-driven), the 30-gene agreement figures, the abundance-only reference (6/10 Plasma) and the constant-guess baseline. The calibration.csv 'l2 | Plasma cell' rows reproduce exactly from cells.parquet.\n\n3. Confounder checks did not remove the effect in the calibrated draws 1-3:\n- excluding samples whose donor trained the draw, the sex_flag sample, and wave-2 samples\n- one sample per donor\n- age+sex adjustment\n- a library-free cell-weighted decomposition\n- label-confidence filters at >= 0.9 and >= 0.99\n- an embedding-free marker-plasmablast definition: generated Plasma-labelled cells are 95-99% marker plasmablasts, and marker plasmablasts carry the MKI67/TOP2A/TYMS/CENPF change\nThe bulk-only plasma-share rise is significant in every draw.\n\n4. Weaker points:\n- The composition-driver sub-claim is 29/30, not all 30, and is unstable for draw 3 under population-matched calibration.\n- csSAM's 10/10 everywhere holds only at the discovery's 40-marker setting and is partly circular.\n- The paired-test carrier agreement for this gene set equals the constant guess.\n- Per-sample plasma calibration is weak (Spearman 0.20-0.34 in d1-d3).\n- Generated plasmablasts under-express cell-cycle genes, so the magnitude is attenuated.\n\nThe 'nothing beyond bulk' conclusion stands, and is if anything stronger than stated: bulk co-expression of these genes with a plasmablast score (rho 0.74-0.89) and a leakage-free 5-donor csSAM recover the same attribution.\n\nOverall: the effect reproduces and survives confounders, the reportable draws are 1-3, and the claim's own 'not new' framing is correct. Because the calibration is only moderate and two sub-statements are overstated, I rate it qualified rather than confirmed.\n\nWrites were confined to my lens dir. CPU only; no GPU work. This verifier published no paper-archive delta; any archive update is pending for the coordinating agent.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v08_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v08_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/vc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v00_explore.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v00_cells_lib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v01_bulk_de.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v01_bulk_cc.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v01_bulk_cc_sens.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v01_bulk_topup.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v02_attribution.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v02_carriers.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v02_agreement.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v02_cc_table.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v03_plasma_share.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v03_bulkonly_effect.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v04_markerpb_effect.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v04_labelfree_carrier.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05_deconv.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05_cssam_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05_reference_carriers.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05_coexpr.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05b_cssam_sweep.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v05b_cssam_sweep.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v06_transfer.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v06_transfer_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v06_covidpos_mix.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v06_crossdraw.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v07_matched_calib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_recompute/v07_matched_calib.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "aaec54a671bf2ef96",
+     "verdict": "qualified",
+     "recomputed": "All numbers below come from my own scripts, written separately from the candidate's ip_common.py. Every test is donor-aware: one unit per donor and side.\n\nBULK\n- I reproduced the bulk-only bulk DE exactly (max |diff − candidate| = 0).\n- 58 vs 19 donor units; 1,118 eligible genes with q < 0.05, of which 382 go up.\n- The 10 genes rank 1–19 among up genes. log2FC is +1.07 to +1.57 and max q is 1.9e-5.\n\nGENE SELECTION\n- An objective cell-cycle set (MSigDB HALLMARK_G2M_CHECKPOINT ∪ HALLMARK_E2F_TARGETS) has 12 genes in the top 30.\n- The claim's list leaves out four of them: BRCA1 (rank 3), LMNB1 (18), MXD3 (20) and STMN1 (24). It adds ASPM and TYMS, which are not in either hallmark set.\n- On the 14-gene set (the 10 plus those 4), paired-test carrier agreement is 2, 12, 13, 12 and 6 of 14 in draws 0–4. Bulk-only vs observed agreement is 10, 14, 12, 14 and 10 of 14.\n\nCARRIER RECOMPUTED (coarse lineages, 300 donor bootstraps)\n- Observed, all paired samples: Plasma carries 10/10 genes (share 0.70–0.97), driven by composition. Bootstrap P(carrier = Plasma) ≥ 0.99.\n- Paired-test agreement, all test samples: 1, 10, 10, 10, 4 of 10 in draws 0–4. Wave-1 test samples only: 1, 10, 10, 10, 9.\n- Bulk-only carrier = Plasma for 8, 10, 10, 10, 8 of 10.\n  - Bootstrap P(Plasma): draw 1 0.95–1.0; draw 2 0.63–1.0; draw 3 0.86–1.0; draws 0 and 4 0.21–0.90.\n  - Removing donor-trained samples, the sex_flag sample, or keeping wave 1 only leaves draws 1–3 at 10/10.\n- Composition-driven in draws 1–3: 30/30 when within-plasma CPM is pooled per side. With the candidate's donor-mean method it is 29/30: TPX2 in draw 3 comes out expression-driven, in their own attribution_genes.csv too.\n\nMAGNITUDE (paired-test samples)\n- Generated whole-sample CPM of the 10 genes is 0.045, 0.127, 0.121, 0.089, 0.061× observed (draws 0–4).\n- The generated COVID+ minus COVID− change of these genes is 3–15% of observed.\n- Per-gene median of generated/observed total change: 0.02, 0.14, 0.10, 0.08, 0.05.\n\nIDENTITY OF THE GENERATED PLASMA CELLS\n- They are real plasma cells: 95–99% pass the 3-of-7 plasma marker rule (observed 99%). JCHAIN, MZB1 and TNFRSF17 CPM are 0.67–1.21× observed.\n- They are mostly not dividing. After matching observed depth to generated, the fraction with any cycling-marker UMI is 0.09–0.19 against 0.37–0.43 observed, a ratio of 0.17–0.44.\n- Within-plasma CPM of the 10 genes, generated/observed: medians 0.05, 0.22, 0.15, 0.15, 0.07.\n\nPLASMA SHARE CALIBRATION\n- calibration.csv row 'l2 | Plasma cell':\n  - ratio 0.23 / 0.66 / 1.21 / 0.61 / 0.67;\n  - Spearman 0.28 / 0.34 / 0.20 / 0.29 / 0.43;\n  - COVID sign correct in 5/5 draws; contrast 0.18 / 0.89 / 1.27 / 0.87 / 0.78× observed;\n  - pooled row: ratio 0.68, Spearman 0.31, generated diff 0.0084 vs observed 0.0107.\n- My donor-level check:\n  - Spearman 0.21 [−0.08, 0.45], 0.47 [0.23, 0.66], 0.18 [−0.07, 0.40], 0.36 [0.11, 0.58], 0.52 [0.29, 0.70];\n  - contrast 0.16, 0.81, 1.15, 0.72, 0.70× observed.\n- Marker-defined plasmablasts, compared with depth-matched observed (observed_thin_d<j>): contrast 0.13, 0.72, 1.02, 0.59, 0.65×.\n- Within COVID+ samples only (so the group effect cannot drive it), Spearman with observed plasma share:\n  - generated cells 0.27–0.45;\n  - 5-gene bulk plasma score 0.67–0.71;\n  - NNLS on bulk with a held-out atlas 0.76–0.79.\n- Within COVID− samples only, generated cells reach −0.33 to 0.18.\n\nBULK-ONLY PLASMA EXPANSION\n- Generated plasma fraction COVID+ minus COVID−:\n  - +0.0029 (3.4×) in draw 0, +0.0127 (6.9×) in draw 1, +0.0159 (4.0×) in draw 2, +0.0096 (8.2×) in draw 3, +0.0081 (4.8×) in draw 4;\n  - p ≤ 6e-4 in every draw, robust to all sensitivity checks.\n- The bulk alone (NNLS) shows it too: +0.032 (8.3×), p = 6e-5.\n\nDECONVOLUTION BASELINES (my own implementation)\n- Composition-only model (NNLS fraction change × atlas profile) on the bulk-only bulk: 10/10 Plasma.\n- Same model on paired-test bulk with a leak-free held-out atlas: 9–10/10.\n- csSAM with marker-gene NNLS: 10/10 on bulk-only; 10, 10, 7, 9, 10 on held-out paired tests. With all-gene NNLS it falls to 0/10, so the fraction estimator matters.\n- Abundance-only atlas: 6/10, with TPX2, TOP2A, KIF11 and CENPF going to T.\n\nTRAINING DONORS\n- Observed plasma fraction in each draw's COVID+ training donors: 0.0044, 0.029, 0.023, 0.011, 0.0079 (cohort mean 0.0158).\n- Their 10-gene CPM, COVID+ vs COVID−: 63 vs 47 (draw 0), 342 vs 69, 209 vs 63, 115 vs 29, 78 vs 80 (draw 4).\n- Draw 4's training donors carry the most proliferating T and NK cells (0.009 and 0.097).\n\nCALIBRATION TRANSFER\n- In bulk-only COVID− samples the 10 genes sit 0.24–0.61 log2 higher than in paired COVID− samples, while plasma genes differ by only −0.06 to +0.24.\n- The COVID log2FC is similar in both sets: 1.07–1.57 bulk-only vs 1.21–1.75 paired.",
+     "issues": [
+      "Size of the signal fails calibration. The generated cells carry only about 5–13% of the observed whole-sample level of the 10 genes and 3–15% of their COVID+ change. The generated plasma cells are real plasma cells by markers (JCHAIN/MZB1/TNFRSF17 at 0.7–1.2× observed) but mostly not dividing: cycling fraction 0.17–0.44× and within-plasma cell-cycle CPM about 0.05–0.22× observed. So 'expansion of plasmablasts' is reproduced as a lineage label, not as proliferation. The Plasma carrier also holds partly because the competing cycling T/NK cells are under-generated too (contrast 0.28–0.66×).",
+      "Gene list was hand-picked. Among the bulk top 30, the hallmark G2M/E2F cell-cycle genes BRCA1 (rank 3), LMNB1, MXD3 and STMN1 were left out, while ASPM and TYMS (not in either hallmark set) were added. BRCA1 and STMN1 are Plasma-carried in observed cells, yet draws 1–3 miss STMN1 (draws 1, 3) or BRCA1 (draw 2). On this 14-gene set, paired-test agreement in draws 1–3 is 12, 13 and 12 of 14, not 10/10.",
+      "Small factual error: 'in draws 1-3 all are composition-driven' is 29/30 by the candidate's own attribution_genes.csv, because TPX2 in draw 3 is expression-driven there. It is 30/30 only with pooled within-plasma CPM.",
+      "The plasma share is weakly tracked per sample. Donor-level Spearman is 0.21 and 0.18 in draws 0 and 2, with CIs that include 0. Within COVID+, generated cells reach ρ 0.27–0.45, against 0.67–0.71 for a 5-gene bulk plasma score and 0.76–0.79 for held-out NNLS. Within COVID−, generated cells reach ρ −0.33 to 0.18. On per-sample plasma share the bulk alone does clearly better than the generated cells.",
+      "Which draws carry it: draws 1 and 3 fully. Draw 2 only partly: carrier and contrast pass, but per-sample tracking fails at donor level and bulk-only bootstrap stability is 0.63 for KIF11 and 0.77 for RRM2. Draw 4 fails the carrier check on all test samples (4/10) but passes on wave-1 test samples (9/10); its bulk-only carriers are unstable. Draw 0 fails both the carrier check and the plasma contrast (0.16×). The training donors explain this: draw 0's COVID+ training donors have almost no plasma expansion and no rise in cell-cycle CPM; draw 4's have high proliferating T/NK cells and no rise in cell-cycle CPM.",
+      "All draws share seed 20260927, so agreement among draws 1–3 is not three independent replications. The claim does not say so.",
+      "Beyond bulk: none. My own composition-only deconvolution (NNLS × atlas) gives 10/10 on the bulk-only bulk and 9–10/10 on paired tests with a leak-free atlas. The cohort paper already made this attribution with CIBERSORTx (Gisby 2022 PDF p.5). The candidate's csSAM 10/10 holds with marker-gene NNLS and a leaky atlas; with a held-out atlas it is 7–10/10, and with all-gene NNLS it is 0/10.",
+      "Not new. Gisby 2022 PDF p.7, Fig. 4a: module tL is labelled 'Plasma cell module', its pathways are cell cycle and its hub genes are RRM2, FOXM1, MKI67, TPX2 and BUB1 (4 of the 10 claimed genes). PDF p.5: tL is linked to CIBERSORTx-imputed plasma-cell proportion. PDF p.2: cell-cycle pathways are up in COVID+. PDF p.7 text: CD38-high plasmablasts. Stephenson 2025 PDF p.3: antibody-secreting B cells, dividing ones included, rise in COVID+. PDF p.4: cell-cycle and DNA-repair pathways are enriched in B cells and antibody-secreting B cells. Bernardes 2020 PDF p.3: proliferating plasmablasts in severe COVID. Huang 2023 PDF p.1 is the contrast case: in MAS the bulk proliferation signal sits in cycling CD38+HLA-DR+ T/NK cells.",
+      "Calibration transfer caveat. Bulk-only COVID− controls (all wave 1) are 0.24–0.61 log2 higher than paired COVID− samples for the 10 genes, with plasma genes about equal. The bulk COVID contrast itself is similar in the two sets."
+     ],
+     "reasoning": "1. Calibration. calibration.csv does not hold the carrier feature, so I recomputed the paired check myself: generated vs observed cells on each draw's test samples, with a donor-level bootstrap.\n- The carrier passes in draws 1–3 (10/10). It fails in draw 0 (1/10) and in draw 4 (4/10, or 9/10 on wave-1 test samples).\n- The plasma-share row in calibration.csv supports the COVID+ direction in 5/5 draws. Its size is right in draws 1–4 (0.78–1.27×) and wrong in draw 0 (0.18×), but per-sample Spearman is weak (0.20–0.43). Pooled: ratio 0.68, Spearman 0.31, contrast 0.78×.\n- What is not calibrated is the amount of signal. The generated cells hold about 5–13% of the observed level of these genes and 3–15% of their COVID change, because the generated plasma cells are mostly not dividing.\n- Calibration therefore supports who carries the signal in draws 1–3, but not how much.\n\n2. Observed data. The observed paired cells support the claim fully. Plasma carries all 10 genes (share 0.70–0.97), the change comes from composition, the bootstrap gives ≥ 0.99, and the plasma share rises with COVID. Nothing in the observed data contradicts the claim.\n\n3. Biology and novelty. The claim is consistent with the cohort papers and already reported in them. Gisby 2022 named RRM2, MKI67, TPX2 and BUB1 as hub genes of its cell-cycle 'Plasma cell module' and linked that module to deconvolved plasma proportion. Stephenson 2025 reported dividing antibody-secreting B cells rising in COVID+.\n\n4. Beyond bulk. Nothing. A plain bulk + atlas composition model reaches 10/10, and bulk markers or NNLS track the per-sample plasma share 2–3× better (in Spearman) than the generated cells do.\n\nThe claim already says it is not new and adds nothing beyond deconvolution. Its stated numbers check out except for one slip (29/30, not 30/30, composition-driven). It is not refuted, but support is only partial for these reasons:\n- only 3 of 5 draws pass the carrier check, and draw 2 only partly;\n- the signal size is about 10× too small;\n- per-sample tracking is weak;\n- the 10 genes were hand-picked;\n- the shared seed was not mentioned.\n\nRead it as a positive control, not a discovery: on a known, bulk-derivable attribution, the generated cells put the right lineage on the signal, with the wrong amount.\n\nI wrote files only to my verify folder. I did no GPU work. This verifier creates no paper-archive change; whether one is needed is for the coordinating agent to decide.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v05_bulkonly_shift.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v05_bulkonly_plasma_shift.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v05_bulk_plasma_shift.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v05_observed_cc_by_type.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v06_marker_plasmablast_calib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v06_marker_plasmablast_calib.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v06_marker_states_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v07_training_donor_plasma.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v07_training_donor_plasma.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v07_training_donor_plasma.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v08_within_group_tracking.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v08_within_group_tracking.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v08_within_group_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v09_tables.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v09_tables.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v09_top30cc_agreement.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-5_calib/v09_g10_within_plasma_ratio.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/features/imperial_eskd/calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/attribution_genes.csv",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Bernardes2020_Megakaryocytes_erythroid_plasmablasts_severe_COVID19.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Huang2023_Type_I_interferon_cycling_lymphocytes_macrophage_activation_syndrome.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-POP-3",
+   "lens": "imp_populations",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 11,
+    "theme": "monocyte HLA class II",
+    "synthesis_strength": "weak: a known, bulk-derivable direction that draws 1, 2, 4 reproduce in wave 1; largely dilution in bulk-only cells",
+    "reportable_statement": "The share of HLA-II-high CD14 monocytes is lower in bulk-only COVID+, severe, fatal and higher-WHO samples in all five draws (severity -0.021 to -0.054), and observed cells agree for COVID, WHO grade and fatal outcome. In bulk-only cells this is largely dilution by a CD14 expansion the generator invents; the absolute number of HLA-II-high CD14 cells per 1,000 cells actually rises with severity. Bulk HLA-DR already carries the signal.",
+    "carrying_draws": {
+     "wave-1 severity and WHO": "1,2,4",
+     "wave-1 COVID": "1,4",
+     "fatal": "none",
+     "all test samples": "0-4 (ratio 0.53-0.71)"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv; recomputed COVID+ ratio 0.53-0.72, Spearman 0.25-0.46; signs 5/5 for COVID, severity, fatal, WHO. Wave 1: severity/WHO pass in draws 1, 2, 4; COVID in 1, 4 (the observed wave-1 COVID difference is null or reversed in draws 2-3). The p90 threshold fails in draws 1-3. Fatal within severe not reproduced (0.11-0.20x; draw 3 wrong sign).",
+     "replication": "5/5 same sign; survives age/sex/days/wave, wave 1, sex flag/training donors, label confidence, matched depth. Adjusted for the CD14 share, severity holds in 2/5 (share only) or 4/5 (with demographics), COVID 2/5, fatal 0/5. Generated quantile shift is dilution-like (median down more than q90); observed loses the upper tail.",
+     "observed": "Observed COVID -0.075 (p 0.0013), severity -0.071 (p 0.019; adjusted -0.034 to -0.038, n.s. at the p75 threshold, significant at p50/p90), fatal -0.121 (p 0.0014), WHO -0.047 per grade (p 3e-5; within donors -0.033, p 0.007).",
+     "beyond_bulk": "None: bulk HLA-DRA/DRB1 alone separates severity in bulk-only donors (AUC 0.80, p 1e-4) and tracks observed CD14 HLA-II-high share better (0.48-0.56 vs 0.32-0.47); the generated partial is about 0 after four bulk scores."
+    },
+    "verdict_reasons": [
+     "Bulk-only severity effect is mostly dilution by an uncalibrated CD14 expansion (+6.8 to +12.9 pp in severe; observed +0.6 pp, p 0.65).",
+     "Fatal is not separate from severity; the generator misses the observed within-severe fatal difference.",
+     "Calibration transfer doubtful for severe samples (more neutrophil/alarmin RNA in bulk-only severe bulk).",
+     "The observed COVID effect is driven by wave 2 (wave 1 -0.039, p 0.12).",
+     "Gate passes narrowly and depends on threshold and label set (CD14+intermediate gives 0.75-1.02).",
+     "Gisby 2022 Fig. 3c already shows bulk HLA-DRA falling with WHO grade in both waves."
+    ],
+    "refuted_subclaims": [
+     "fewer HLA-II-high cells (in absolute terms)"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 3C and p.8 (PDF p.6, p.9); Gisby 2022 Fig. 3c (PDF p.6); COMBAT 2022 p.922; Schulte-Schrepping 2020 p.1427; the sepsis run found the same direction (SEP-POP-2).",
+    "next_steps": [
+     "Report absolute counts and share-adjusted versions next to shares.",
+     "Calibrate on wave-1 samples and with several thresholds."
+    ],
+    "key_sources": [
+     "verify/IMP-POP-3_recompute/tables/v03_parent_share.csv",
+     "verify/IMP-POP-3_recompute/tables/v03_adjust_parts.csv",
+     "verify/IMP-POP-3_recompute/tables/v07_quantiles.csv",
+     "verify/IMP-POP-3_recompute/tables/v04_feature_calibration.csv",
+     "verify/IMP-POP-3_calib/tables/v04_gates.csv",
+     "verify/IMP-POP-3_calib/tables/v03_A_tracking.csv",
+     "verify/IMP-POP-3_calib/tables/v02_truth.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-POP-3",
+    "cohort": "imperial_eskd",
+    "claim": "Generated CD14 monocytes of bulk-only samples have fewer HLA class II-high cells in COVID+, severe and fatal cases and at higher WHO grade. The observed paired cells agree for COVID, WHO grade and fatal outcome, but the observed severity effect is not significant after covariate adjustment. The generator under-produces the state with only modest per-sample tracking, and bulk HLA-DR already carries the signal.",
+    "kind": "state",
+    "feature": "Share of CD14-labelled monocytes with HLA-DRA+HLA-DRB1 > 70.4 CP10k (75th percentile of observed COVID- CD14). Embedding-free, all cells, thinned. Coherent markers in both domains: HLA-DPA1/DPB1, HLA-DQB1, HLA-DMA, CD74, CPVL up; S100A8/9/12, CTSD, PLBD1 down (logFC Spearman 0.56 pooled, 0.38-0.44 per draw). Cluster counterpart: Mono r1.0 c6 (HLA-II-high, 64% 'Intermediate monocyte' label; Jaccard 0.59-0.63 on re-embedding; marker rho 0.89). Level: observed native 16.4%; observed thinned 16.0-17.7%; paired_gen 9.3-11.1%; bulk-only 8.0-9.3%.",
+    "comparison": "Severe/critical vs mild/moderate (COVID+); fatal vs non-fatal; WHO grade at sample; COVID+ vs COVID-.",
+    "effect_bulkonly": "Severity, by draw: d0 0.070 vs 0.091 (-0.021), d1 0.057 vs 0.082 (-0.025), d2 0.055 vs 0.100 (-0.044), d3 0.063 vs 0.101 (-0.038), d4 0.058 vs 0.112 (-0.054). Fatal: -0.018 to -0.046. WHO grade: -0.013 to -0.023 per grade. COVID: -0.028 to -0.057.",
+    "stats": "Donor-level Mann-Whitney per draw; BH over the 17 rules. Severity p 4e-5 to 0.042 (q 0.0001-0.088). Adjusted (days, age, sex, wave; donor-clustered OLS) -0.023 to -0.050, p 0.0001-0.028 (5/5). Fatal p 0.0009-0.076 (4/5); adjusted 3/5. WHO grade p<=0.002 (5/5). COVID p 0.0002-0.045 (5/5). Sensitivity set: severity p<0.0001 to 0.070 (4/5).",
+    "draws_same_sign": "5/5 for all contrasts. The gate passes in 5/5 draws (ratio >=0.53). Draw agreement is inflated by shared sampler noise (same-slot OR 11.9 vs 1.5 shifted).",
+    "paired_calibration": "Ratio 0.65/0.55/0.58/0.61/0.66 (all) and 0.69/0.53/0.54/0.61/0.72 (COVID+). Spearman 0.33/0.46/0.41/0.40/0.39 and 0.25/0.42/0.45/0.35/0.40 (COVID+). Severity, generated vs observed: -0.044/-0.065/-0.053/-0.045/-0.065 vs -0.083/-0.050/-0.056/-0.076/-0.076 (5/5). Fatal: -0.03 to -0.065 vs -0.11 to -0.13 (5/5, 0.3-0.5x). WHO grade: -0.028 to -0.046 vs -0.040 to -0.054 (5/5). The generator shifts the whole CD14 HLA-II distribution down (HLA-II-low share 1.26-1.42x observed).",
+    "paired_observed_concordance": "Partly. Observed native cells, donor level: COVID -0.075 (p=0.0013). Severity -0.071 (p=0.019), but adjusted -0.038 (p=0.12) and wave 1 -0.061 (p=0.21), both not significant. Fatal -0.121 (p=0.0014); adjusted -0.046 (p=0.057). WHO grade -0.047 per grade (p=3e-5); wave 1 -0.056 (p=1.5e-4).",
+    "beyond_bulk": "Not shown. Bulk HLA-DRA/DRB1 alone separates severity in bulk-only donors (p=1e-4; AUC 0.80). The generated CD14 fraction adds to the extended bulk baseline in only 2/5 draws (d2 p=0.024, d4 p=0.0055), and the observed fraction does not add (p=0.17). The all-monocyte version adds in 4/5 draws (p 0.004-0.041), but its generated severity contrast exceeds the observed one and the observed version adds nothing (p=0.38). This added signal is therefore not validated.",
+    "novelty": "Known. HLA class II loss in severe ESKD monocytes: Stephenson 2025 (Fig 3C, p.6; p.8). Classical-monocyte HLA-DR loss with severity: COMBAT 2022 (p.922). HLA-DRhi CD83hi monocytes in mild disease: Schulte-Schrepping 2020 (p.1427). The sepsis run found the same direction (SEP-POP-2).",
+    "strength": "weak",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s07_rules.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_results.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s15_finding_numbers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08b_increment_extended.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_bulk_score_assoc.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s13_rule_marker_concordance.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s02_profile_Monocyte_r1.0.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s06_stability.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "adf8563a31e92941f",
+     "verdict": "qualified",
+     "recomputed": "All numbers are from my own code, which reads the raw compact files directly; tables are in tables/ of my dir.\n\nREPRODUCTION (exact)\n- Threshold: 75th percentile of HLA-DRA+DRB1 CP10k in observed native CD14 cells of COVID- paired samples = 70.42 (9,735 cells) [v01_threshold.csv].\n- Mean levels [v01_levels.csv]:\n  - observed native 0.164\n  - observed thinned 0.164-0.173\n  - paired_gen 0.093-0.111\n  - bulk-only 0.080-0.093\n- Bulk-only severity, donor level, Mann-Whitney [v02_tests.csv]:\n  - d0 0.0695 vs 0.0907 (-0.021, p=0.042)\n  - d1 0.0574 vs 0.0819 (-0.025, p=0.027)\n  - d2 0.0553 vs 0.0995 (-0.044, p=7.8e-4)\n  - d3 0.0628 vs 0.1006 (-0.038, p=3.7e-4)\n  - d4 0.0576 vs 0.1119 (-0.054, p=1.4e-5)\n  - Permutation p: 0.036 / 0.017 / 4e-4 / 0.0016 / 2e-4\n- COVID+ vs COVID-: -0.045 / -0.056 / -0.035 / -0.028 / -0.057 (5/5 at p<0.05).\n- Fatal: -0.018 to -0.046 (4/5 at p<0.05).\n- WHO grade, GEE slope: -0.014 to -0.021 per grade, p<=7.7e-4 (5/5).\n- Adjusted for days, age, sex and wave (GEE): severity -0.026 to -0.058, p<=0.006 (5/5) [v02_adjusted_summary.csv].\n- Calibration on COVID+ test samples (d0-d4) [v04_feature_calibration.csv]:\n  - ratio 0.67 / 0.54 / 0.55 / 0.61 / 0.71\n  - Spearman 0.29 / 0.42 / 0.42 / 0.35 / 0.38\n  - severity, generated vs observed thinned: -0.044/-0.065/-0.054/-0.046/-0.065 vs -0.071/-0.061/-0.060/-0.076/-0.077 (5/5 same sign)\n  - fatal: -0.03 to -0.065 vs -0.10 to -0.13\n  - WHO: -0.018 to -0.041 vs -0.038 to -0.048\n  - HLA-II-low share is 1.31-1.42x observed\n- Observed native truth [v06_truth.csv]:\n  - COVID -0.075 (p=0.0013)\n  - severity -0.071 (p=0.019); adjusted -0.034 (p=0.23); wave 1 -0.061 (p=0.21)\n  - fatal -0.121 (p=0.0014)\n  - WHO -0.040 per grade (p=1e-5); within donor -0.033 (p=0.007)\n- Bulk HLA-DRA+DRB1 alone separates severity in bulk-only donors: AUC 0.79, p=1e-4 [v05_baseline.csv].\n- Shared noise: same-slot odds ratio 11.9 vs 1.49 shifted [v07_shared_noise.csv].\n- Co-markers: all 11 named markers move in the stated direction in 5/5 draws; logFC Spearman 0.38-0.48 [v08_marker_concordance.csv].\n\nSURVIVES (severity, draws at p<0.05)\n- wave 1 only: 5/5\n- excluding the sex_flag sample and training-donor samples: 4/5 (d1 p=0.070)\n- days 3-21: 5/5\n- each CD14 cell thinned to the same library (L=500): 4/5\n- label confidence >= 0.9: 4/5\n- six-type gate agreement: identical result\n- minimum 5 parent cells: 5/5\n- Library size differs between groups in inconsistent directions across draws, so it does not drive the effect.\n\nCONFOUNDER THAT EXPLAINS MUCH OF IT: CD14 expansion (dilution)\n- Severe vs mild CD14 share of all cells [v03_parent_share.csv]:\n  - bulk-only: +0.068 / +0.071 / +0.113 / +0.129 / +0.072 (p<=0.007, 5/5)\n  - observed: +0.006 (p=0.65)\n  - paired_gen: +0.03 to +0.10\n- It is part of a generated composition shift [v07_composition_by_severity.csv]:\n  - bulk-only: T cells -0.11 to -0.22, monocytes +0.055 to +0.16\n  - observed: T cells -0.037 (p=0.43), monocytes -0.005 (p=0.83)\n- calibration.csv row `l2 | CD14 monocyte`, severity: generated +0.022 to +0.060 vs observed -0.010 to +0.008.\n- Absolute HLA-II-high CD14 cells per 1,000 cells, severe vs mild:\n  - bulk-only: +2.5 / +1.8 / +4.0 / +2.4 / +0.4 (up; p=0.011 and 0.0065 in d0 and d2)\n  - observed: -3.2\n- Within CD14 cells, the HLA-DR distribution moves differently [v07_quantiles.csv]:\n  - bulk-only severe: median -13% to -37%, q90 only -9% to -19% (dilution-like)\n  - observed severe: q90 -22% to -25% (loss of the upper tail)\n- Adjusted for CD14 share [v03_adjust_parts.csv]:\n  - severity, share only: -0.014 to -0.029, p<0.05 in 2/5 (d2, d4)\n  - severity, demographics + share: 4/5 (d0, d1, d2, d4)\n  - fatal, demographics + share: 0/5\n  - WHO, demographics + share: 4/5\n  - COVID, share only: 2/5 (d0, d1) [v07_adjust_covid_tshare.csv]\n  - The observed effects do not change under the same adjustments.",
+     "issues": [
+      "The bulk-only effect is largely dilution by a CD14 expansion the generator invents. Severe samples get +6.8 to +12.9 pp more CD14-labelled cells (5/5 draws), alongside 11-22 pp fewer T cells, while observed paired cells show no expansion (+0.6 pp, p=0.65). The official calibration row `l2 | CD14 monocyte` shows the generator's severity contrast at +0.022 to +0.060 against an observed value near zero.",
+      "The absolute number of HLA-II-high CD14 cells per 1,000 cells goes up with severity in bulk-only samples (+0.4 to +4.0), the opposite of observed (-3.2). So 'fewer HLA class II-high cells' is true only as a share. The HLA-DR distribution also moves differently: in generated severe samples the median drops more than the 90th percentile (dilution-like), while in observed severe samples the upper tail is lost.",
+      "After adjusting for CD14 share, the severity effect shrinks by roughly 25-60%:\n- share only: significant in 2/5 draws (d2, d4)\n- with demographics: significant in 4/5 draws\n- COVID contrast, share only: significant in 2/5 draws (d0, d1)\n- fatal, demographics + share: significant in 0/5 draws\nThe observed effects are unchanged by the same adjustments.",
+      "The fatal effect is not separate from severity. Every fatal donor is severe/critical, and fatal vs non-fatal within severe donors is significant in only 1/5 draws (-0.005 to -0.019). On paired test samples the generator does not reproduce the observed within-severe fatal difference (generated -0.013 to +0.016 vs observed -0.10 to -0.14; wrong sign in d3). Observed fatal donors are 7/8 from wave 2.",
+      "Calibration may not transfer to severe bulk-only samples. Their bulk has more neutrophil signal (+1.34 log2, p=0.0045), more alarmin (+0.59, p=0.009) and lower HLA-DR (-0.31, p=0.089) than the paired severe samples used for calibration. The generated CD14 share tracks bulk alarmin (rho 0.58-0.77) and runs inversely to the bulk T-cell signal, while the bulk monocyte score does not change with severity in either cohort (p=0.61 and 0.27). This fits the generator turning the whole-blood neutrophil/S100 signal into extra HLA-II-low CD14 cells; the dilution is stronger in bulk-only than in paired_gen.",
+      "Two gaps in the claim's observed concordance:\n- The observed COVID effect is driven by wave 2 (wave 1: -0.039, p=0.12; wave 2: -0.136, p=4e-5), but 88% of bulk-only samples are wave 1.\n- The observed severity effect is not significant in wave 1 (-0.061, p=0.21) or after adjustment (wave 1 adjusted: -0.008, p=0.88).",
+      "Repeated-measures checks weaken it:\n- Using only each donor's first visit, severity is significant in 3/5 draws (d0 p=0.052, d1 p=0.43).\n- Within-donor WHO slopes are negative in 5/5 draws but significant in only 2/5 (d0, d2), whereas the observed within-donor slope is significant (-0.033, p=0.007).",
+      "Calibration is weak to moderate: ratio 0.54-0.71 on COVID+ test samples (d1 and d2 close to the 0.5 gate) and Spearman 0.29-0.42. The share's own gate passes in 5/5 draws, but the parent composition it depends on is not calibrated.",
+      "No added value over bulk is shown. The generated share adds to bulk HLA-DR alone in 3/5 draws (d1, d2, d4) and to the extended bulk baseline in 2/5 (d2 p=0.028, d4 p=0.035). The observed share adds nothing (p=0.38 and 0.20).",
+      "Minor discrepancies:\n- The claim's severity p range starts at 4e-5, but d4 is 1.4e-5 (the discovery's own table shows 1e-5).\n- The q-values depend on a 17-rule BH family I did not recompute. BH over this feature's 5 tests gives severity q<0.05 in 4/5 draws (d0 q=0.07).\n- In draw 2, 6 of 19 COVID- donors fall below 20 CD14 cells and are dropped.\n- The cluster counterpart (Mono r1.0 c6) and the novelty statement were not re-verified."
+     ],
+     "reasoning": "Everything in the claim reproduces from raw files with independent code, and the effect survives most standard confounder controls. But one confounder the claim never mentions explains much of it, so the verdict is \"qualified\" rather than \"confirmed\".\n\nWhat reproduces, to rounding:\n- the threshold, all four levels and the per-draw group means and differences;\n- the signs (5/5 draws for every contrast) and the significance pattern;\n- the calibration ratio and Spearman, within thinning-seed noise;\n- the observed truth, the bulk AUC and the shared-noise odds ratio;\n- the co-marker coherence.\n\nWhat it survives:\n- age, sex, days since swab and wave (GEE, 5/5 draws);\n- wave 1 only (5/5);\n- excluding the one sex_flag sample and samples whose donor trained the draw (4/5);\n- equal per-cell library (4/5);\n- label confidence >= 0.9 (4/5) and gate agreement;\n- the minimum-cell filter.\nLibrary-size differences between groups go in inconsistent directions across draws.\n\nWhat the claim misses: in bulk-only samples the generator gives severe samples about twice as many CD14-labelled cells as mild ones, as part of a lymphopenia-plus-monocytosis shift. The observed paired cells of this cohort show none of it, and the official `calibration.csv` row for CD14 share confirms the generator's severity contrast is miscalibrated. Because these extra cells are HLA-II-low, the HLA-II-high share drops by dilution. In absolute terms, the HLA-II-high CD14 cells per 1,000 cells actually rise in severe bulk-only samples, the opposite of observed.\n\nControlling for CD14 share leaves partial results:\n- severity: all 5 draws still negative, about half the size; significant in 2/5 (d2, d4) with share alone, 4/5 (d0, d1, d2, d4) with demographics;\n- WHO grade: 4/5 (d0-d3);\n- COVID: 2/5 (d0, d1);\n- fatal: 0/5. Fatal also adds nothing beyond severity (within-severe 1/5), and the generator fails the within-severe calibration.\n\nCalibration transfer is doubtful for severe samples. Their bulk is more neutrophil- and alarmin-rich than that of the paired severe samples, and the CD14 expansion tracks bulk alarmin, not the bulk monocyte score.\n\nSo the effect holds only in part. Per-draw gates on the share itself pass 5/5, but the defensible residual is a smaller severity / WHO-grade signal in d0-d4 (strongest in d2 and d4) on top of a dilution artifact. It still adds little beyond bulk HLA-DR, and on the observed side only the WHO-grade association is robust.\n\nProcess: CPU only under the guide's 6G memory-capped scope; I checked that MemAvailable was at least 48 GiB before loading subsamples. All writes stayed in my verify dir. I published no paper-archive update; that task-end step belongs to the coordinator.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v00_checks.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v01_fractions.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v02_bulkonly_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v03_parent_share.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v04_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/v05_bulk.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/tables/v02_summary.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/tables/v04_label_boundary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/tables/v05_transfer.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/tables/v06_truth.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_recompute/logs"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "afb387d9d36860735",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt everything from cells.parquet and markers.npz with my own code (vlib.py, v01-v04). The 'CD14 HLA-II-high' feature is not in calibration.csv, so I computed the equivalent paired check myself.\n\nTHRESHOLD AND LEVELS\n- Threshold reproduced: p75 = 70.42 CP10k (9,735 reference cells). Thinning factors match: 0.813, 0.691, 0.629, 1.0, 0.676.\n- Levels: observed native 16.4%; observed thinned 16.4-17.6%; paired_gen test 9.3-11.1%; bulk-only 8.0-9.3%.\n\nCALIBRATION, ALL TEST SAMPLES (gen = paired_gen draw j, obs = observed thinned to draw j, mean of 5 thinning seeds)\n- COVID+ ratio by draw: 0.68 / 0.53 / 0.53 / 0.61 / 0.71. Thinning-seed ranges: d1 0.517-0.536, d2 0.521-0.534.\n- Sample-level Spearman: all 0.32 / 0.47 / 0.41 / 0.40 / 0.40; COVID+ 0.27 / 0.45 / 0.46 / 0.35 / 0.41.\n- Donor-level Spearman (COVID+): 0.24 / 0.47 / 0.50 / 0.34 / 0.32.\n- Severity, gen vs obs: -0.044 vs -0.077; -0.065 vs -0.059; -0.054 vs -0.076; -0.046 vs -0.076; -0.065 vs -0.079.\n- Fatal: generated effect is 0.23-0.52x the observed one.\n- WHO grade: -0.028 to -0.046 vs -0.042 to -0.052 per grade.\n- COVID: -0.019 to -0.068 vs -0.038 to -0.066.\n- Gate (same sign, rho >= 0.2, ratio 0.5-2) passes in 5/5 draws for COVID, severity, fatal and WHO grade.\n- Fatal within severe donors is not reproduced: generated 0.11-0.20x observed, and d3 has the opposite sign.\n\nCALIBRATION, THRESHOLD AND LABEL VARIANTS\n- p90 threshold: ratio 0.35-0.62, gate fails in d1-d3. The generator compresses the HLA-II-high tail.\n- CD14 + Intermediate monocytes: ratio 0.75-1.02, rho 0.56-0.67.\n\nCALIBRATION, WAVE-1 TEST SAMPLES ONLY (bulk-only samples are 88% wave 1)\n- Severity and WHO grade pass the gate only in d1, d2 and d4. d0 fails on rho (0.14); d3 fails on ratio (0.47).\n- COVID passes only in d1 and d4. The observed COVID difference is +0.003 in d2 and +0.008 in d3, so the sign is reversed there.\n- Fatal cannot be tested: only 1 fatal paired donor in wave 1.\n\nBULK-ONLY, REPRODUCED PER DRAW\n- Severity: -0.021 / -0.024 / -0.044 / -0.038 / -0.054; p = 0.042 / 0.027 / 7.8e-4 / 3.7e-4 / 1.4e-5.\n- Sensitivity set (sex_flag and training-donor samples dropped), severity p = 0.048 / 0.070 / 6.7e-4 / 3.7e-4 / 1.0e-5.\n- Adjusted severity p = 0.028 / 0.0025 / 1.7e-4 / 9.1e-4 / 6.3e-5.\n- Fatal: -0.018 to -0.046; adjusted p < 0.05 in 3/5 draws.\n- WHO grade: -0.013 to -0.023 per grade, p <= 0.002 in all 5 draws.\n- COVID: -0.028 to -0.057, p 1.5e-4 to 0.045.\n- Fatal within severe: p 0.05-0.30.\n\nOBSERVED NATIVE CELLS (TRUTH)\n- COVID -0.075 (p=0.0013); severity -0.071 (p=0.019); fatal -0.121 (p=0.0014); WHO grade -0.047 per grade (p=2.9e-5).\n- Adjusted (days, age, sex, wave): severity p=0.12, fatal p=0.057, WHO grade p=2.8e-7.\n- Adjusted severity is significant with other definitions: p50 p=0.045, p90 p=0.033, CD14+Intermediate p=0.014.\n- Wave 1, donor level: COVID -0.039 (p=0.12), severity -0.061 (p=0.21), WHO grade -0.056 (p=1.5e-4).\n- Wave-by-contrast interaction is not significant: COVID p=0.19, severity p=0.79, WHO grade p=0.51.\n- Fatal within severe donors: -0.127 (p=0.13; 8 vs 5 donors).\n\nBEYOND BULK (paired test samples)\n- Bulk HLA-DRA/DRB1 (log CPM) alone tracks the observed feature: rho 0.53 / 0.48 / 0.54 / 0.56 / 0.53.\n- The generator tracks it less well: rho 0.32 / 0.47 / 0.41 / 0.40 / 0.40.\n- A leave-donor-out bulk ridge model reaches 0.62-0.69. It uses about 50 donors' sc data, so it is only an upper reference, not a fair comparison.\n- Partial rho of generated vs observed:\n  - given bulk HLA2: 0.06 / 0.33 / 0.22 / 0.12 / 0.19;\n  - given HLA2 + MONO + BCELL + DC: -0.12 / 0.11 / 0.03 / -0.10 / 0.01;\n  - given the ridge prediction: -0.06 to 0.08.\n- Bulk-only severity association adjusted for bulk HLA2: p = 0.83 / 0.20 / 0.039 / 0.17 / 0.023 (2/5). Adding the cell scores leaves 1/5 (d4 p=0.033).\n- Bulk HLA2 alone, bulk-only donors: severity p=9.1e-5 (AUC 0.80), WHO grade p=9e-8, COVID p=1.6e-4.\n- In observed paired cells, the sc fraction adds fatal information beyond bulk HLA2 + cell scores (p=0.0056). The generator does not reproduce that fatal-within-severe component.\n- Calibration transfer: bulk HLA2, HLAII6, MONO, BCELL and DC do not differ between paired and bulk-only donors within any group (p > 0.29). The wave mix differs: paired severe/critical samples are 64% wave 2, bulk-only are 88% wave 1.",
+     "issues": [
+      "The 'beyond bulk' leg is more negative than 'not shown'. On paired test samples, bulk HLA-DRA/DRB1 alone ranks samples by their observed CD14 HLA-II-high share better than the generator in every draw (rho 0.48-0.56 vs 0.32-0.47; d1 is close, 0.48 vs 0.47). After controlling for 4 bulk gene-set scores, the generator's partial rho is about 0 (-0.12 to 0.11). Per sample, the generated feature is a noisier proxy of bulk HLA-DR.",
+      "The calibration does not transfer cleanly. Bulk-only samples are 88% wave 1, while paired severe/critical samples are 64% wave 2. On wave-1 test samples only, the gate passes for severity and WHO grade in 3/5 draws (d1, d2, d4) and for COVID in 2/5 (d1, d4). In d2 and d3 the observed COVID contrast in wave 1 is null or reversed (+0.003, +0.008) while the generator still produces a decrease. Fatal cannot be calibrated in wave 1 (only 1 fatal donor).",
+      "Fatal is not an independent agreeing contrast. In bulk-only data all 12 fatal donors are severe, and fatal within severe donors is not significant in 4/5 draws. The generator does not reproduce the observed fatal-within-severe effect (0.11-0.20x observed; d3 has the opposite sign). The fatal result therefore reduces to the severity result.",
+      "The level gate passes only narrowly and depends on the threshold. The COVID+ ratio is 0.52-0.53 in d1 and d2 at p75. At p90 the ratio is 0.35-0.62 and the gate fails in d1-d3, so the generator compresses the high-HLA-II tail. The CD14-only label also lowers the ratio; CD14+Intermediate gives 0.75-1.02.",
+      "The observed severity result is threshold-dependent in the opposite direction from the claim's caveat. At p75 the adjusted severity effect is not significant (p=0.12), but it is significant at p50 (0.045), at p90 (0.033) and with CD14+Intermediate (0.014). The caveat is conservative, not wrong.",
+      "Wave-2 confounder: Stephenson 2025 (printed p.10 / PDF p.11) reports dexamethasone-induced HLA-DRA/DRB5-low, CD163-high monocytes only in the 2021 (wave-2) cohort, in severe/critical patients. This treatment effect contributes to the observed wave-2 severity and fatal contrasts. In paired wave-2 donors, severe and fatal are identical (7 vs 8). The generator does not make these dex monocytes.",
+      "The novelty section misses the bulk precedent from this same cohort. Gisby 2022 Fig. 3c (p.6) shows bulk PBMC HLA-DRA falling stepwise from COVID-negative through mild, moderate and severe to critical, significant at 1% FDR in both waves. The generator's input already carried this signal. Stephenson 2025 ref. 18 (EBioMedicine 2023) also reports blood HLA-DRA as a COVID-19 prognostic marker.",
+      "Page citations mix printed and PDF numbering. Stephenson 2025 Fig 3C text is on printed p.5 (PDF p.6). The temporal HLA class II downregulation in severe/critical course is on printed p.8 (PDF p.9), with the Fig 4C heatmap on printed p.7. COMBAT 2022 p.922 (mass-cytometry cMono HLA-DR falls with severity) and Schulte-Schrepping 2020 p.1427 (HLA-DRhi CD83hi monocytes in all mild cases; HLA-DRA/DRB1 low early and sustained in severe) are correct.",
+      "Draw agreement is not five independent replications. Draws share sampler noise: for this state, same-slot OR is 8.9-14 vs 0.9-1.8 for shifted slots (candidate s12). Under the wave-1 gate only d1, d2 and d4 carry the severity and WHO-grade results, and only d1 and d4 carry COVID."
+     ],
+     "reasoning": "Neither the calibration nor the observed data contradicts the claim, so it is not refuted. Support is partial and the finding adds nothing beyond bulk, so it is qualified, not confirmed.\n\n(1) Calibration. The feature is not in calibration.csv, so I computed the check independently. Generated paired_gen draw j was compared with observed cells thinned to draw j's CD14 library (5 seeds), on draw j's test samples.\n- Direction is reproduced in 5/5 draws for COVID, severity, fatal and WHO grade.\n- The level is under-produced (ratio 0.53-0.71) and per-sample tracking is modest (rho 0.27-0.47).\n- The trust gate passes in 5/5 draws on the full test set, but narrowly in d1 and d2, and it fails at a p90 threshold.\n- The fatal contrast is attenuated, and fatal within severe donors is not reproduced.\n- Following DATA_GUIDE's rule to recalibrate on the analysed population (bulk-only is 88% wave 1), severity and WHO grade pass in d1, d2 and d4 only; COVID passes in d1 and d4 only.\n- These draws carry the result: d1, d2, d4 for severity and WHO grade; d1, d4 for COVID; no draw for a fatal-specific effect.\n\n(2) Observed support. Observed native cells agree in direction for all four contrasts: COVID p=0.0013, severity p=0.019, fatal p=0.0014, WHO grade p=3e-5. The WHO-grade effect also survives adjustment and holds within wave 1. Adjusted severity is not significant at p75 (p=0.12) but is significant with p50, p90 or CD14+Intermediate. The wave-1 tests are weaker, but the wave interactions are not significant, which points to low power rather than a contradiction. Fatal is confounded with severity and, in wave 2, with dexamethasone.\n\n(3) Biology and novelty. The finding is well known in this exact cohort.\n- Single-cell: Stephenson 2025 reports monocyte HLA-DPB1/DPA1/DRB1/DRA/DQA1 down and S100A8/9/12, MCEMP1 and CD163 up in severe/critical (Fig 3C, printed p.5), and steep HLA class II loss over time with a severe course (printed p.8).\n- Bulk: Gisby 2022 Fig. 3c (p.6) shows HLA-DRA falling with WHO grade, significant in both waves.\n- Other cohorts: COMBAT 2022 p.922 and Schulte-Schrepping 2020 p.1427 report the same direction.\n- Nothing contradicts the claim, and nothing about it is new.\n\n(4) Beyond bulk. The generator adds nothing. Bulk HLA-DR alone predicts the observed sc feature better than the generator does. The generator's information is fully explained by 4 bulk gene-set scores. Only d2 and d4 keep a severity association after adjusting for bulk HLA2.\n\nThe claim's own hedges ('weak', 'bulk already carries the signal', 'not validated') are justified and should be strengthened as listed in the issues. Recommended framing: a known, bulk-derivable direction that a subset of draws reproduces, not a single-cell discovery.\n\nI wrote only under the verifier directory and did no GPU work. I did not make a pbisc-diffusion archive update: my write scope is limited to this directory, so maintenance belongs to the coordinating agent, and there is no archive delta from this verifier.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v01_depth.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v02_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v03_bulk_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v03_A_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v03_B_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v03_C_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v04_gates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v04_levels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/tables/v04_wave_interaction.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/logs/v01_fracs.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/logs/v02_calib_tests.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/logs/v03_bulk.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-3_calib/logs/v04_summary.log",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/COMBAT2022_Blood_atlas_COVID19_severity.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/SchulteSchrepping2020_Severe_COVID19_dysregulated_myeloid.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-CLIN-3",
+   "lens": "imp_clinical",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 12,
+    "theme": "platelets",
+    "synthesis_strength": "moderate as an echo of a real observed association; not an independent bulk-only measurement",
+    "reportable_statement": "Generated platelets are 1.5-2.9x more frequent in severe/critical than in mild/moderate bulk-only COVID+ patients (+1.77, +1.25, +1.19, +0.71, +1.04 logit) and rise with WHO grade between patients; observed paired platelets show the same association. Calibration over both waves supports draws 1-2 (size) and 3-4 (direction), but in wave 1 (88% of bulk-only samples) generated platelet levels are 2.4-19x too high, and most of the bulk-only association rides on the bulk granulocyte (LDG/neutrophil) signal.",
+    "carrying_draws": {
+     "peak severity, both waves": "1,2 PASS; 3,4 DIR; 0 FAIL (level 4.2x)",
+     "wave 1": "none on level (3 DIR)",
+     "beyond bulk platelet+LDG+neutrophil scores": "none calibrated"
+    },
+    "honesty": {
+     "calibration": "calibration.csv severity row: ratio 3.66/0.71/0.84/0.51/1.63, Spearman 0.29-0.41, gates FAIL, PASS, PASS, DIR, DIR (COVID+-only: FAIL, PASS, PASS, DIR, DIR; synthesis i01). Wave-1 level 19.4, 6.0, 5.2, 2.4, 10.4x; the relative contrast is reproduced (0.74-1.82x).",
+     "replication": "5/5 draws; survives wave, training donors, days window, library size, label confidence, ethnicity/diabetes and the bulk platelet score. Draw 3 is fragile (unadjusted p 0.13; no-sc donors p 0.25; LDG p 0.056). Bulk platelet + LDG + neutrophil + wave adjustment removes 48-72%; 10 bulk PCs remove it. Shared noise 6-26% (half-split), above the claimed 5.3%.",
+     "observed": "Observed peak +1.76 (p 2e-4); with wave +1.51 (p 1e-6); wave 1 +1.28 (p 0.008); WHO +0.72 (p 1.5e-5), within donors +0.64 (p 4e-4). Observed platelets are about 10x more frequent in wave 2 than wave 1 (single-cell processing site).",
+     "beyond_bulk": "Beyond a platelet-gene score yes (that score is weak in bulk-only samples: +0.31-0.34 SD, p 0.14-0.21), beyond granulocyte + platelet scores only partly and uncalibrated (paired truth keeps +0.80 to +0.97, generated draws 1-4 +0.11 to +0.54), beyond the whole bulk no (LDG9 alone donor AUC 0.767 >= generated 0.62-0.76)."
+    },
+    "verdict_reasons": [
+     "Calibration is full only in draws 1-2; wave-1 levels fail in every draw.",
+     "Generated platelets track bulk neutrophil signal (rho 0.38-0.73) far more than observed platelets (0.15-0.24).",
+     "Transfer doubtful: bulk platelet genes are not linked to severity in bulk-only samples, unlike paired samples (+0.73-0.77 SD).",
+     "'Does not track severity within a patient' is wrong: within-donor WHO is positive in 5/5 and nominally significant in 3/5.",
+     "'Platelets are most of Other' is false in draws 1-3 (30-42%); in draws 3-4 the 'Other' rise is largely progenitors.",
+     "Draw 3 depends on sex/days adjustment."
+    ],
+    "refuted_subclaims": [
+     "does not track severity changes within a patient",
+     "platelets make up most of 'Other'"
+    ],
+    "new_vs_cohort_papers": "Not reported as a cell fraction for this cohort (Stephenson 2025 does not mention platelets; Gisby 2022 pathway-level: complement/coagulation and RUNX1 platelet-function proteome p.5; PF4/PF4V1 in convalescence p.10). Established in other cohorts' PBMC single cells: COMBAT 2022 p.4, Bernardes 2020 pp.3 and 13, Stephenson 2021 p.1, Hottz 2020 p.2.",
+    "next_steps": [
+     "Wave-1 single-cell platelet truth (or a processing-site-adjusted target) before any level claim.",
+     "Require the effect beyond bulk platelet + granulocyte scores on paired truth."
+    ],
+    "key_sources": [
+     "verify/IMP-CLIN-3_recompute/v02_family.csv",
+     "verify/IMP-CLIN-3_recompute/v04_confounders.txt",
+     "verify/IMP-CLIN-3_recompute/v05_calibration.txt",
+     "verify/IMP-CLIN-3_recompute/v07_partial_calib.txt",
+     "verify/IMP-CLIN-3_calib/v01_calib_recomputed.csv",
+     "verify/IMP-CLIN-3_calib/v02_observed.txt",
+     "verify/IMP-CLIN-3_calib/v06_tracking.txt",
+     "verify/IMP-CLIN-3_calib/v09_fullbulk.txt",
+     "imp_clinical/s16_platelet_by_wave.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-CLIN-3",
+    "cohort": "imperial_eskd",
+    "claim": "Generated platelets, which make up most of the six-type 'Other' class, are 1.5–2.8× higher in severe/critical than in mild/moderate COVID+ patients and rise with WHO severity at sampling between patients (5/5 draws; draws 1–4 calibrated). Observed cells agree. The association goes beyond a platelet-gene bulk score but not beyond the whole bulk, part of it rides on the bulk neutrophil (LDG) signal, and it does not track severity changes within a patient.",
+    "kind": "composition",
+    "feature": "l2 | Platelet (supporting: l1 | Other = platelets + erythrocytes + progenitors + ILC; 30–75% platelets in generated cells, s06)",
+    "comparison": "Peak severity (severe/critical vs mild/moderate) among COVID+, plus WHO severity at sample. Both between donors.",
+    "effect_bulkonly": "Peak-severity logit, draws 0–4: +1.77, +1.25, +1.19, +0.71, +1.04. Severe/mild fraction: 0.0375/0.0154, 0.0085/0.0030, 0.0095/0.0046, 0.0033/0.0022, 0.0212/0.0107. WHO slope: +0.77, +0.60, +0.52, +0.41, +0.52 per step; between-donor +0.39 to +0.94; within-donor +0.21 to +0.47, not significant. l1 Other, peak: +0.58 to +1.15.",
+    "stats": "Model: MixedLM logit ~ severe_peak (or who) + age/10 + male + d + d² + (1|donor). Data: n = 143 COVID+ samples from 58 donors. Peak severity: p 1.1e-7, 1.9e-6, 1.5e-5, 3.2e-3, 5.6e-6; q 2.7e-6, 1.9e-5, 1.2e-4, 0.010, 2.2e-5. WHO: q 4.4e-7, 6.3e-7, 4.5e-5, 9.4e-4, 3.8e-7. Robust to S1–S5 (including a wave covariate and the ≤21-day window), ethnicity + diabetes, and the alternative models. No-single-cell donors (S6): peak severity 4/5 draws p ≤ 0.01 (draw 3 p 0.25). After adjusting for the bulk LDG score: peak +0.29 to +0.66 (4/5 p < 0.05); WHO +0.24 to +0.37 (5/5 p ≤ 0.008).",
+    "draws_same_sign": "5/5. Carrying draws: 1–4. Draw 0 fails on level, generating platelets at 4.2× observed. The 'Other' class carries in 0–4. Shared seed noise explains 5.3% (platelet) and 3.7% (Other) of cross-draw agreement.",
+    "paired_calibration": "calibration.csv, severe vs mild row: ratio 3.66/0.71/0.84/0.51/1.63; Spearman 0.41/0.38/0.41/0.40/0.29; gen diff +0.023/+0.007/+0.007/+0.004/+0.006 vs obs +0.007/+0.011/+0.012/+0.012/+0.012; same sign 5/5; gates FAIL, PASS, PASS, DIR, DIR. COVID+ vs COVID− row: same sign 5/5, but the donor-aware observed COVID effect is null (p 0.09–0.19; wave 1 p 0.57), so no COVID claim is made (gates NULL_genfx). Recomputed peak, donor-aware: FAIL (level 4.24), PASS, PASS, DIR, DIR (gen +0.77 to +1.89 vs obs +1.55 to +1.89). Recomputed WHO: FAIL, PASS, PASS, DIR, PASS. Wave-1 restriction: the severity contrast is reproduced (effect ratio 0.74–1.8), but the level gate fails (generated 2.4–19× observed). The reason is that observed wave-1 single cells contain about 10× fewer platelets: COVID+ 0.14% (wave 1) vs 1.41% (wave 2). Single-cell processing differed by wave (Stephenson 2025 PDF p. 20), while generated levels do not differ by wave.",
+    "paired_observed_concordance": "Yes. Observed peak severity +1.76 (p 2.1e-4, q 2.5e-3; 113 samples, 35 donors); 1.64% vs 0.32%. With a wave covariate +1.51 (p 1.1e-6); wave 1 only +1.28 (p 0.008). Observed WHO +0.72 per step (p 1.5e-5). Observed cells also show a within-donor WHO effect, +0.64 (p 4e-4), which the bulk-only generated cells do not reproduce (+0.21 to +0.47, not significant), although the generator reproduces it on the paired test samples.",
+    "beyond_bulk": "Partly. The platelet-gene bulk score (PPBP, GP9, TUBB1, PF4 …) is not significantly associated in bulk-only samples: peak +0.31 SD (p 0.21), WHO +0.19 SD (p 0.07). The generated fraction is associated and survives the bulk score: +0.51 to +1.04 SD. Donor-level AUC for peak severity: bulk score 0.59, generated 0.65–0.81 (LRT p 3.5e-6 to 0.015). In paired data the observed platelet fraction carries severity information beyond the bulk score (+0.84 SD, p 7e-4), and the generator reproduces that partial association on the paired test samples (peak: 4/5 draws p < 0.05; WHO: 5/5). Three limits: (1) the generated platelet fraction correlates with the bulk LDG/neutrophil score (ρ 0.32–0.63, observed 0.24), and adjusting for LDG shrinks the effect by about 35–55%; (2) it adds nothing to a full-bulk model (leave-one-donor-out AUC with 10 bulk PCs + age/sex: peak 0.743 vs 0.740–0.760; fatal 0.770 vs 0.736–0.795); (3) absolute platelet levels depend on the single-cell processing batch.",
+    "novelty": "The biology is known, but the cell fraction is not reported for this cohort. Gisby 2022 PDF pp. 5–6: complement and coagulation pathways up with severity in the PBMC bulk; proteomic 'RUNX1 regulates … platelet function'. Gisby 2022 PDF p. 10: PF4/PF4V1/PPBP up into convalescence. Stephenson 2025 does not mention platelets. Platelet activation and platelet–leukocyte aggregates in severe COVID-19 are widely reported elsewhere (not checked against a local PDF).",
+    "strength": "moderate",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s02_bulkonly_assoc.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s03_paired_calib.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s06_other_composition.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s07_sensitivity.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_beyond_bulk_checks.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_ldg.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_fullbulk_cv.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_partial_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s13_extra_gates.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s16_platelet_by_wave.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s15_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a2f747865e595aeeb",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt the per-sample counts from the raw cells.parquet of every set; they show 0 mismatches against composition.parquet. Data: bulk-only COVID+, 143 samples from 58 donors.\n\nPlatelet (L2), peak severity. MixedLM logit ~ sev + age10 + male + d1 + d2 + (1|donor), draws 0–4:\n- Effect: +1.77, +1.25, +1.19, +0.71, +1.04.\n- p: 1.1e-7, 1.9e-6, 1.5e-5, 3.2e-3, 5.6e-6.\n- BH q over 50 features: 2.7e-6, 1.9e-5, 1.2e-4, 0.010, 2.2e-5.\n- Severe/mild mean fraction: 0.0375/0.0154, 0.0085/0.0030, 0.0095/0.0046, 0.0033/0.0022, 0.0212/0.0107. Ratios 2.43, 2.88, 2.05, 1.50, 1.98.\n\nWHO at sample:\n- Slope: +0.77, +0.60, +0.52, +0.41, +0.52 (q 4.4e-7 to 9.4e-4).\n- Between donors: +0.94, +0.74, +0.71, +0.39, +0.56.\n- Within donors: +0.47 (p 0.034), +0.27 (0.18), +0.21 (0.23), +0.46 (0.032), +0.45 (0.0044).\n\nl1 Other, peak severity: +1.15, +0.58, +0.78, +0.59, +0.90.\n\nAlternative estimators all keep the same sign in 5/5 draws:\n- GEE, donor-clustered OLS, binomial with donor-clustered SE, donor aggregation;\n- donor permutation (p ≤ 0.009); one sample per donor; rank-transformed y; dropping the top 5% of samples;\n- leave-one-donor-out: smallest coefficient +0.63, largest p 0.0084.\n- Unadjusted, draw 3 is +0.37 (p 0.13). A draw-3-specific male coefficient (−0.93, p 3e-4) masks it.\n\nConfounder controls (peak severity):\n- Significant (p < 0.05) in 5/5 draws:\n  - wave covariate;\n  - wave 1 only: +1.64, +1.10, +1.06, +0.55 (p 0.034), +1.08;\n  - dropping sex_flag samples, samples whose donor trained the draw, or both;\n  - days ≤ 20 or ≤ 21;\n  - generated library size (draw 2 shrinks 57% to +0.51, p 0.013);\n  - label confidence: confidence covariate, and platelet calls with confidence ≥ 0.9;\n  - ethnicity + diabetes; bulk raw library size; bulk platelet score.\n- Significant in 4/5, draw 3 losing it each time:\n  - no-sc donors (p 0.25);\n  - bulk LDG score (p 0.056); bulk mature-neutrophil score (p 0.056);\n  - non-fatal donors only (p 0.061).\n- Bulk platelet + LDG + neutrophil scores together: +0.74, +0.64, +0.51, +0.20, +0.44, a shrink of 48–72% (draw 3 p 0.38).\n- 10 bulk PCs: −0.02 to +0.22, significant in 0/5.\n\nCalibration, calibration.csv severity row:\n- Ratio 3.66/0.71/0.84/0.51/1.63; Spearman 0.41/0.38/0.41/0.40/0.29.\n- Generated/observed difference ratio 3.43/0.58/0.55/0.30/0.48.\n- My gate: draw 0 fails on level, draws 1–2 PASS, draws 3–4 direction only.\n\nCalibration recomputed on COVID+ test samples, donor-aware:\n- Generated vs observed severity effect: 1.89 vs 1.55, 1.12 vs 1.58, 1.41 vs 1.89, 1.34 vs 1.87, 0.77 vs 1.75.\n- Level ratio 4.24/0.76/0.90/0.45/1.52.\n- Wave 1 only: level 19.4×, 6.0×, 5.2×, 2.4×, 10.4×; generated/observed effect ratio 0.74–1.82.\n\nPaired test samples, adjusted for bulk platelet + LDG + neutrophil scores and wave:\n- Observed: +0.82, +0.80, +0.97, +0.95, +0.90 (p ≤ 0.004).\n- Generated: +1.03, +0.23, +0.54, +0.46, +0.11 (draws 1–4 p 0.08–0.7).\n\nObserved cells, 113 samples from 35 donors:\n- Peak severity +1.76 (p 2.1e-4); with wave +1.51 (1.1e-6); wave 1 only +1.28 (0.008); with log cell number +1.69; binomial +2.10.\n- WHO +0.72 (1.5e-5); within donors +0.64 (4.2e-4).\n\nBulk scores vs peak severity:\n- Bulk platelet score: bulk-only +0.33 SD (p 0.17), paired +0.73 SD (p 0.005).\n- Bulk LDG score: bulk-only +0.99 SD (p 5e-6), paired +0.46 SD (p 0.10).\n- Spearman of the platelet logit with the bulk mature-neutrophil score: generated 0.38–0.62 (bulk-only), 0.53–0.57 (paired generated); observed 0.15.\n\nDonor-level leave-one-donor-out AUC for peak severity:\n- age + sex + 10 PCs: 0.730; adding the generated fraction: 0.729–0.746.\n- Generated fraction alone: 0.65–0.81. Bulk platelet score alone: 0.60.\n\nShared seed noise:\n- Same-slot platelet calls co-occur across draws 16–52× more often than chance.\n- Half-split estimate of the shared-noise share of cross-draw agreement: 6–26% (mean 14%).\n\nIdentity of the generated platelet cells:\n- Platelet-gene share of their library is 0.15–0.20 (observed 0.16–0.18).\n- Granulocyte-gene share is about 0.003 and not higher in severe samples.\n\nComposition of 'Other':\n- Platelets make up 75/42/34/30/55% of bulk-only 'Other'.\n- In draws 3–4 the severe-group rise in 'Other' is largely progenitor cells: share 0.30 → 0.47 and 0.12 → 0.27.",
+     "issues": [
+      "Calibration is full only in draws 1–2. Draws 3–4 reproduce the direction only: the generated/observed difference ratio is 0.30 and 0.48, and the level ratio 0.51 and 1.63. Draw 0 fails on level (4.2×). The headline 'draws 1–4 calibrated' overstates this. In wave 1, which is 88% of the bulk-only samples, the level gate fails in every draw (generated 2.4–19× observed).",
+      "The 'beyond the bulk' part is largely bulk granulocyte (LDG + mature neutrophil) signal, and the remainder is not calibrated. Adjusting for the bulk platelet, LDG and neutrophil scores shrinks the bulk-only effect by 48–72% (draw 3 p 0.38). On paired truth after the same adjustment, observed cells keep +0.80 to +0.97 (p ≤ 0.004), but generated cells in draws 1–4 give only +0.11 to +0.54 (p 0.08–0.7). Generated platelets track the bulk neutrophil score (ρ 0.38–0.62) far more than observed platelets do (ρ 0.15).",
+      "Calibration transfer is doubtful. Bulk platelet genes are not significantly linked to severity in bulk-only samples (+0.33 SD, p 0.17), though they are in paired samples (+0.73 SD, p 0.005). The bulk-only severity contrast is mainly LDG/neutrophil (+0.99 SD). Within wave 1, severe bulk-only samples trend toward less platelet signal (−0.35 SD, p 0.17) and more LDG signal (+0.30 SD, p 0.066) than severe paired samples. So the bulk-only contrast reaches 'platelets' mostly through the granulocyte route.",
+      "The statement 'does not track severity changes within a patient' is wrong. Within-donor WHO estimates are positive in 5/5 draws, with nominal p 0.034, 0.18, 0.23, 0.032 and 0.0044. Four of the five 95% intervals contain the observed +0.64. 'Not significant' holds only after BH across 50 features. Within-donor power is similar to the observed set: 19 vs 17 donors whose WHO changes. The correct reading is weaker, inconsistent within-donor tracking, not absent tracking.",
+      "'Platelets make up most of Other' is false for the calibrated draws 1–3, where they are 30–42% of 'Other'. In draws 3–4 the severe-group rise in 'Other' comes mostly from progenitor cells, which observed cells do not show. 'Other' is therefore not clean supporting evidence for platelets.",
+      "Draw 3 depends on adjustment. Unadjusted it gives +0.37 (p 0.13), with a donor-level rank test p of 0.052, masked by a draw-3-specific male effect (−0.93, p 3e-4). It also loses significance in no-sc donors (p 0.25), with the LDG or neutrophil score (p 0.056) and in non-fatal donors (p 0.061).",
+      "Shared seed noise is understated. My half-split estimate of its share of cross-draw agreement is 6–26% (mean 14%), against the claimed 5.3%. Same-slot platelet calls co-occur 16–52× more often than chance. Each draw's effect is strong on its own, so this does not threaten the effect, but '5/5 draws' are not five independent replications.",
+      "In draw 2 the effect shrinks 57% when the generated library size is adjusted for (still p 0.013). In draws 1–2, severe samples get shallower generated cells.",
+      "Minor numbers: the severe/mild ratio range is 1.5–2.9× (draw 1 is 2.88×), not 1.5–2.8×. The draw-0 generated difference in calibration.csv is 0.0225, not 0.023."
+     ],
+     "reasoning": "The finding is qualified, not refuted. Everything that reproduces and survives:\n- Every number in the claim reproduces from my own code on the raw cells: effects, p and q values, means, the WHO between/within split, the 'Other' effects, the calibration rows, the wave-1 restriction, the observed-cell concordance and the leave-one-donor-out AUC.\n- The bulk-only severity effect on generated platelets has the same sign in 5/5 draws under every estimator.\n- It survives every reasonable clinical or technical confounder control: age, sex, wave (as a covariate and in wave 1 only), sex_flag samples, samples whose donor trained the draw, the days window, ethnicity/diabetes, generated and bulk library size, cell number, label confidence, one sample per donor, leave-one-donor-out, and rank or outlier robustness.\n- The paired observed cells show the same severity effect, robust to wave and cell number.\n- The generated platelet cells are platelet-like by their own expression, so this is not a labelling artefact.\n\nThe claim needs qualifying in five places:\n1. **Calibration.** It passes fully only in draws 1–2; draws 3–4 reproduce only the direction, and the level fails in wave 1 for every draw.\n2. **Granulocyte route.** The effect rides heavily on the bulk granulocyte (LDG/neutrophil) signal. Its part beyond the bulk platelet, LDG and neutrophil scores is not reproduced on paired truth in draws 1–4. The generator over-couples platelets to bulk neutrophil genes compared with observed cells, so 'goes beyond a platelet-gene bulk score' means little more than adding the granulocyte signal. The claim's own concession ('not beyond the whole bulk', 'partly LDG') holds, and the LDG dependence is larger than stated.\n3. **Transfer.** The bulk-only cohort's severity contrast lacks the bulk platelet-gene support seen in the paired cohort.\n4. **Overstated statements.** The within-donor null, 'most of Other' and the 5.3% shared-noise figure are not supported.\n5. **Draw 3.** It is fragile: it needs the sex and days adjustment.\n\nHonesty summary:\n- Calibration: PASS in draws 1–2, direction only in 3–4, level failure in 0.\n- Replication: 5/5 draws, but they share seed noise.\n- Observed concordance: yes.\n- Beyond bulk: beyond the bulk platelet score yes; beyond bulk granulocyte + platelet scores only partly and uncalibrated; beyond the whole bulk no.\n\nAll work was CPU-only through systemd-run (MemoryMax 6G, zero swap, CUDA_VISIBLE_DEVICES empty). No GPU work was started, nothing was written outside the lens directory, and this verifier published no archive delta; that is left to the coordinator.",
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+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "abf7cf093d611a960",
+     "verdict": "qualified",
+     "recomputed": "All numbers below come from my own scripts (v01–v10) in the verifier dir. None of the lens code was imported.\n\nCALIBRATION, official calibration.csv, l2|Platelet, severe vs mild row:\n- ratio 3.66/0.71/0.84/0.51/1.63; Spearman 0.41/0.38/0.41/0.40/0.29\n- gen diff +.0225/+.0066/+.0065/+.0036/+.0058 vs obs +.0065/+.0113/+.0118/+.0122/+.0120\n- gates FAIL/PASS/PASS/DIR/DIR (pooled ratio 1.47, Spearman 0.38)\n\nCALIBRATION, recomputed on COVID+ paired test samples (MixedLM logit, donor random intercept, + wave covariate):\n- level 4.24/0.76/0.90/0.45/1.52; Spearman 0.55/0.45/0.48/0.50/0.39\n- peak severity: gen +1.88/+1.13/+1.43/+1.36/+0.80 vs obs +1.38/+1.35/+1.60/+1.65/+1.49 (p≤3e-5); gates FAIL/PASS/PASS/DIR/PASS\n- WHO between donors: gen +1.70/+1.12/+1.21/+1.16/+0.77 vs obs +1.25 to +1.41\n- WHO within donors: gen +0.58/+0.70/+0.60/+0.41/+0.50 vs obs +0.66 to +0.75\n\nCALIBRATION, wave 1 only (126 of the 143 bulk-only COVID+ samples are wave 1):\n- level 19.4/6.0/5.2/2.4/10.4; Spearman 0.51/0.51/0.56/0.60/0.45\n- peak severity: gen +2.17/+1.15/+1.33/+1.21/+0.97 vs obs +1.19/+1.05/+1.30/+1.59/+1.31\n- gates FAIL/FAIL/FAIL/DIR/FAIL; every failure is on level only\n\nOBSERVED paired cells (113 COVID+ samples, 35 donors):\n- peak severity +1.76 (p 2e-4); with wave +1.51 (p 1e-6), leave-one-donor-out range +1.40 to +1.62\n- wave 1 only +1.28 (p 0.008); wave 2 only +1.78 (p 1e-4); donor AUC 0.79\n- WHO +0.72 (p 2e-5); within donor +0.64 (p 4e-4)\n- 1.64% vs 0.32%; platelet fraction wave 2 / wave 1 = 10.35×\n\nBULK-ONLY generated (143 samples, 58 donors):\n- peak severity +1.77/+1.25/+1.19/+0.71/+1.04; BH q over 50 features 2.7e-6/1.9e-5/1.2e-4/1.0e-2/2.2e-5\n- with wave +1.72/+1.21/+1.17/+0.66/+1.03; wave 1 only +1.64/+1.10/+1.06/+0.55/+1.08\n- donors with no single cells (31 donors): +1.19/+1.01/+0.81/+0.31 (p 0.3)/+1.07\n- adjusted for LDG9: +1.02/+0.80/+0.72/+0.34 (p 0.2)/+0.53\n- adjusted for LDG9 + PLT21 + wave: +0.81/+0.64/+0.54/+0.23 (p 0.3)/+0.47\n- WHO within donor (+wave): +0.49 (p .03)/+0.28 (.2)/+0.21 (.2)/+0.48 (.03)/+0.45 (.004). The lens's raw p values are 0.034/0.18/0.23/0.032/0.0044, with BH q 0.11 to 0.87.\n- donor AUC 0.81/0.79/0.76/0.65/0.77; severe/mild fraction ratio 2.43/2.88/2.05/1.50/1.98×\n\nBULK SCORES vs peak severity (my gene sets):\n- PLT21 platelet genes: paired +0.77 SD (p 0.005); bulk-only +0.34 (p 0.14); bulk-only wave 1 +0.19 (p 0.43)\n- LDG9 neutrophil genes: paired +0.65 (p 0.04); bulk-only +0.92 (p 1e-6)\n\nWHAT THE GENERATED FRACTION READS FROM THE BULK (paired test samples, OLS):\n- generated: b_LDG 0.42–0.59, b_PLT 0.18–0.40\n- observed: b_LDG 0.28–0.36, b_PLT 0.34–0.41\n- Spearman with LDG9: generated 0.57–0.73 vs observed 0.24\n- generated fraction does not follow the wave shift on paired test samples: wave 2 / wave 1 = 0.85–1.16×, against observed 10.4×\n\nCELL IDENTITY: generated 'Platelet' cells:\n- PPBP+ 94–96%, PF4+ 91–95%, ≥2 of 5 platelet markers 99%, ≥2 LDG markers ~0%\n- observed platelets thinned to the same depth: 95%/95%/99%\n\nPLATELET SHARE of generated 'Other' (bulk-only): 0.75/0.42/0.34/0.30/0.55; observed 0.43.\n\nFULL BULK (donor-level leave-one-donor-out AUC):\n- LDG9 alone 0.767\n- generated fraction alone 0.618–0.761\n- stacked full-bulk ridge 0.737–0.754; adding the generated fraction changes it by −0.02 to +0.05\n- likelihood-ratio test of generated over LDG9 + PLT21: p 0.024/0.007/0.057/0.26/0.078",
+     "issues": [
+      "Calibration that matches the bulk-only population fails on level. 126 of the 143 bulk-only COVID+ samples are wave 1. On wave-1 paired test samples, generated platelets are 19.4/6.0/5.2/2.4/10.4× observed, so draws 0, 1, 2 and 4 FAIL and only draw 3 passes (DIR). 'Carrying draws 1–4' holds only for the calibration over both waves. The relative contrast is still reproduced in wave 1 (effect ratio 0.74–1.82; Spearman 0.45–0.60).",
+      "The wave-1 platelet deficit is not purely a single-cell processing artefact. The bulk platelet score, measured on fresh PBMC, is also lower in wave 1 (paired COVID+ +0.06 vs +0.41). The generator ignores this shift on paired test samples (generated wave 2 / wave 1 = 0.85–1.16×, against observed 10.4×). So its platelet level is not anchored to the bulk platelet signal. Processing did differ by wave (Stephenson 2025 PDF p.20).",
+      "In bulk-only samples, the direct bulk evidence for a severity difference in platelets is weak: PLT21 +0.34 SD (p 0.14), and in wave 1 +0.19 (p 0.43). In paired samples the same score gives +0.77 (p 0.005). The generated association instead rides mainly on the neutrophil (LDG) signal, which is more strongly tied to severity in bulk-only (+0.92 SD) than in paired samples (+0.65). The generator also weights LDG more than the observed cells do (b_LDG 0.42–0.59 vs 0.28–0.36). Adjusting for LDG9 + PLT21 + wave removes 54–68% of the effect, and draw 3 becomes non-significant. This is a calibration-transfer risk.",
+      "'Does not track severity changes within a patient' is overstated. The within-donor WHO effect is positive in 5/5 draws (+0.21 to +0.49) and nominally significant in draws 0, 3 and 4 (p 0.004–0.034). It is non-significant only after BH over 50 features (q 0.11–0.87). It is attenuated to about 0.3–0.75× of the observed +0.64, not absent.",
+      "'Platelets make up most of Other' is true only in draws 0 (75%) and 4 (55%). In the calibrated draws 1–3 platelets are 30–42%, and in draw 3 progenitor cells (39%) outnumber platelets. The supporting 'l1 | Other' feature is therefore mostly non-platelet in those draws.",
+      "Adds nothing beyond the bulk. A 9-gene LDG bulk score alone gives donor-level leave-one-donor-out AUC 0.767 for peak severity, at least as good as every generated platelet feature (0.618–0.761). Stacked full-bulk ridge gives 0.737–0.754, and adding the generated fraction changes it by −0.02 to +0.05. 'Beyond a platelet-gene score' is literally true but uninformative, because that score is itself a poor marker in bulk-only samples (AUC 0.48).",
+      "Novelty is narrower than stated. Local PDFs already report that the platelet / megakaryocyte fraction in PBMC single-cell data rises with severity: COMBAT 2022 p.4 (CITE-seq; rises with WHO ordinal severity and with thromboembolism), Bernardes 2020 pp.3 and 13, Stephenson 2021 p.1 and Hottz 2020 p.2. For this cohort, Stephenson 2025 never mentions platelets (0 hits). Gisby 2022 reports only complement and coagulation pathways and a RUNX1 platelet-function proteome pathway with severity (p.5), and PF4/PF4V1/PPBP in convalescence (p.10). The observed paired cells already show the association directly in 35 donors.",
+      "Agreement across draws is not independent replication: every draw reads the same bulk and uses seed 20260927. I did not recompute the lens's 5.3% shared-noise share.",
+      "Draw behaviour has a traceable cause. Draw 0's level failure (4.2×) comes from training donor C141 (wave 2, severe, 7.6% observed platelets). Draw 3, the weakest, was trained on the donors with the lowest platelet content (mean 0.48%)."
+     ],
+     "reasoning": "Nothing refutes the claim, but the support is partial, so the verdict is qualified.\n\n1. Observed paired cells (all four checks below are reported, as the guide requires): the association is robust. Peak severity gives +1.51 logit with a wave covariate (p 1e-6), and the leave-one-donor-out range stays at +1.40 to +1.62. It holds within each wave (wave 1 +1.28, p 0.008; wave 2 +1.78, p 1e-4), and WHO at sample also has a within-donor effect.\n\n2. Paired calibration over both waves supports the direction and roughly the size in draws 1–4. Draw 0 fails on level (4.24×).\n\n3. In the wave-1 subset that matches 88% of the bulk-only samples, the level gate fails in 4 of 5 draws (generated 5–19× observed). Only the relative effect survives there.\n\n4. The generated cells really are platelet-like by markers: PPBP+ 94–96%, PF4+ 91–95%, 99% express at least 2 of 5 platelet markers, and almost none express neutrophil markers.\n\n5. The model gets there partly by a different route than the real cells. Its output leans on the bulk neutrophil (LDG) score more than observed platelets do. In bulk-only samples the platelet-gene bulk score barely separates severe from mild patients, and adjusting for LDG + platelet score + wave removes 54–68% of the effect.\n\n6. The generated fraction adds no reliable predictive value over the bulk: a 9-gene neutrophil score alone matches or beats it.\n\n7. The within-patient statement is overstated: the effect is attenuated, not absent (nominal p < 0.05 in 3 of 5 draws).\n\n8. The biology is already established in other cohorts' PBMC single-cell data (COMBAT 2022 p.4, Bernardes 2020 pp.3 and 13, Stephenson 2021 p.1, Hottz 2020 p.2). The cohort's own papers do not report it (Stephenson 2025: no platelet mentions; Gisby 2022: pathway-level signals only, pp.5 and 10).\n\nSafe reportable form: the observed platelet fraction rises with peak and WHO severity in this cohort. The bulk-only model reproduces that association in draws 1–4 on the calibration over both waves, but it does not stand as an independent cell-level measurement in the bulk-only patients.\n\nAll work ran on CPU under the guide's memory limits (MemAvailable stayed at 94 GiB or more). Nothing was written outside the verifier dir and no paper asset changed, so there is no archive delta; the maintenance publish step belongs to the coordinator.",
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+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-POP-2",
+   "lens": "imp_populations",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 13,
+    "theme": "monocyte CD163 / glucocorticoid",
+    "synthesis_strength": "weak: a known CD163 level shift reproduced at about half strength in draws 1 and 3; not a distinct state; glucocorticoid link unresolved",
+    "reportable_statement": "The share of generated CD14 monocytes with CD163 detected rises with severity, WHO grade and fatal outcome in bulk-only patients (severity +0.043, +0.056, +0.029, +0.127, +0.119; draw 2 p 0.088), echoing a real observed association (+0.217, p 2.6e-4). Draws 0-2 reproduce about half the observed level and contrast; draw 4 rides on a CD14 composition artefact and its training donor. The 'HLA-II-low FKBP5+ RNASE2+ PLBD1+ state' and 'not dexamethasone-induced' parts are not supported.",
+    "carrying_draws": {
+     "robust": "1,3",
+     "calibrated but composition-confounded": "4",
+     "borderline": "0,2"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv; recomputed COVID+ ratio 0.51/0.55/0.53/0.67/1.09, Spearman 0.50-0.65, contrast 0.44-0.83x. Draws 0-2 fail with a depth-robust CP10k level (0.39-0.44) or a >=2-count rule; wave-1 gate passes in draws 1, 3, 4.",
+     "replication": "5/5 positive; survives age/sex/wave/days, sex flag/training donors, first sample per donor, label confidence. Depth-matched detection keeps the sign (draws 0, 2 p 0.088, 0.059); adjusting for CD14 share removes draw 4 (+0.119 -> +0.024). In the 31 COVID+ donors with no single cells, severity is significant only in draw 4 (p 0.034).",
+     "observed": "Observed severity +0.217 (p 2.6e-4), adjusted +0.206; wave 1 +0.213 (p 0.029; leave-one-donor-out p 0.010-0.075); fatal +0.223; WHO +0.136 per grade (p 1e-5). Observed CD163+ cells are not HLA-II-low-enriched (37% vs 38.5%; within-sample OR 0.71-0.79); FKBP5, RNASE2, PLBD1 do not co-occur with CD163 within samples.",
+     "beyond_bulk": "Generated: none against 4-6-gene bulk baselines (LR p 0.41-0.98). The observed raw fraction adds to the bulk for severity (permutation p 0.021) but not after depth matching (p 0.09-0.27)."
+    },
+    "verdict_reasons": [
+     "Calibration weak in draws 0-2; draw 4 confounded by CD14 over-production and training-donor imprint (C33: 74% CD163+).",
+     "'Mostly HLA-II-low' is a generator-wide HLA-II deficit, not a property of the state.",
+     "The marker list comes from between-sample differences; observed CD163 detection is near-Poisson, so the share reads per-sample CD163 level.",
+     "Wave-1 severe monocytes carry a glucocorticoid programme (ADAMTS2 +2.9, FKBP5 +0.95 log2) and dex-like cells (16.4%); FKBP5 absorbs about half of the wave-1 effect.",
+     "Fatal adds nothing beyond severity.",
+     "Two quoted numbers do not trace (WHO +0.103; 64% ISG+)."
+    ],
+    "refuted_subclaims": [
+     "mostly HLA-II-low",
+     "FKBP5+/RNASE2+/PLBD1+ state",
+     "not dexamethasone-induced (distinct from dex-monos)"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 3C (CD163 up with severity; PDF p.6) and dex-monos (PDF p.11); Schulte-Schrepping 2020 p.1427; COMBAT 2022 p.922 (FKBP5.CD163 module).",
+    "next_steps": [
+     "Obtain steroid exposure per sample.",
+     "Use a depth-robust CD163 level, not a detection share.",
+     "Train draws without steroid-era CD163-rich donors to see whether the effect survives."
+    ],
+    "key_sources": [
+     "verify/IMP-POP-2_recompute/tables/v07_summary_by_draw.csv",
+     "verify/IMP-POP-2_recompute/tables/v02b_tech_adjust.csv",
+     "verify/IMP-POP-2_recompute/tables/v03_calib.csv",
+     "verify/IMP-POP-2_recompute/tables/v06_wave1_loo.csv",
+     "verify/IMP-POP-2_calib/tables/v06_quoted_numbers.csv",
+     "verify/IMP-POP-2_calib/tables/v01_calibration.csv",
+     "verify/IMP-POP-2_calib/tables/v02_within_sample_or.csv",
+     "verify/IMP-POP-2_calib/tables/v04_w1_gc_adjust.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-POP-2",
+    "cohort": "imperial_eskd",
+    "claim": "Generated CD14 monocytes of bulk-only samples contain more CD163+ cells (mostly HLA-II-low; FKBP5+, RNASE2+, PLBD1+) with severity, WHO grade and fatal outcome. The observed paired cells show this association in both waves, so it is not only dexamethasone-induced. The generator produces the state at about 0.5x the observed level and contrast in draws 0-2, misses its IL1R2/MCEMP1/THBS1 arm, and its fraction adds nothing beyond bulk CD163 expression (the observed single-cell fraction does add).",
+    "kind": "state",
+    "feature": "Share of CD14-labelled monocytes with CD163 detected (>=1 count). Subset: CD163+ HLA-II-low (HLA-DRA+DRB1 < 23.1 CP10k). Embedding-free, all cells, observed thinned per draw. Observed markers (CD163+ HLA-low vs other CD14): IL1R2, FKBP5, THBS1, MCEMP1, DDIT4, RNASE2, PLBD1, IFI27 up; HLA-DP/DQ/DM, LGALS2 down. Generated cells reproduce FKBP5, RNASE2, PLBD1, IFI27 and STAB1 but not IL1R2, MCEMP1 or THBS1 (logFC Spearman, generated vs observed: 0.60 pooled, 0.38-0.54 per draw). Level: observed native 30.8%; observed thinned 23-31%; paired_gen 13-25%; bulk-only 13.6-27.5%. About 64% of CD163+ HLA-low cells are ISG+, so this overlaps IMP-POP-1.",
+    "comparison": "Severe/critical vs mild/moderate (COVID+ donors); fatal vs non-fatal; WHO grade at sample; COVID+ vs COVID-.",
+    "effect_bulkonly": "Severity, donor means by draw: d0 0.187 vs 0.144 (+0.043), d1 0.195 vs 0.139 (+0.056), d2 0.156 vs 0.127 (+0.029), d3 0.297 vs 0.171 (+0.127), d4 0.356 vs 0.237 (+0.119). Fatal: +0.059 to +0.195 (d4 0.442 vs 0.256). WHO grade: +0.019 to +0.078 per grade. COVID: +0.045 to +0.140. Subset CD163+ HLA-II-low: severity +0.023 to +0.085 (5/5 p<0.05); fatal +0.036 to +0.134.",
+    "stats": "Donor-level Mann-Whitney per draw; BH over the 17 rules. Severity p 0.0002-0.088 (q 0.0009-0.17; 4/5 below 0.05, d2 p=0.088). Fatal p 0.0001-0.0038 (q<=0.009, 5/5). WHO grade p<=0.003 (5/5). Adjusted for days since swab, age, sex and wave (donor-clustered OLS): severity +0.029 to +0.135, p<=0.008 (5/5). Wave 1 only: draws 1, 3 and 4 p<=0.031; draws 0 and 2 p 0.22/0.42. Sensitivity set (sex-flag and training-donor samples removed): severity p 0.0001-0.16 (4/5); fatal p 0.0001-0.0075 (5/5).",
+    "draws_same_sign": "5/5 for severity, fatal, WHO grade and COVID. The gate passes in 5/5 draws but at the lower ratio bound in draws 0-2. Draw agreement is inflated by shared sampler noise. Draw 4 has the highest level (1.04-1.09x); its training donor C33 (severe, fatal, wave 2) has 74% CD163+ CD14 monocytes.",
+    "paired_calibration": "Ratio 0.51/0.57/0.53/0.65/1.04 (all) and 0.51/0.55/0.53/0.67/1.09 (COVID+). Spearman 0.50/0.53/0.55/0.58/0.50 and 0.53/0.63/0.62/0.62/0.64 (COVID+). Severity contrast, generated vs observed: +0.096/+0.092/+0.098/+0.144/+0.143 vs +0.218/+0.195/+0.195/+0.238/+0.182; sign 5/5, magnitude 0.44-0.79x. Fatal: +0.08 to +0.13 vs +0.17 to +0.23 (5/5). WHO grade: +0.041 to +0.102 vs +0.130 to +0.149 (5/5). calibration.csv: 'MS1 monocyte (IL1R2+)' has ratio 0.04-0.37, so the IL1R2 arm of this state is not reproduced. Not a dexamethasone-monocyte effect: a rule for CD163 plus >=1 of ADAMTS2/VSIG4/SLC1A3/MFGE8/MRC1 gives generated at 0.02-0.09x of observed (Spearman 0.18-0.59), and these genes are emitted in <=0.7% of generated monocytes vs 0.7-4.2% observed.",
+    "paired_observed_concordance": "Yes, strongly. Observed native cells, donor level: severity +0.217 (p=2.6e-4; 13 vs 21); adjusted +0.206 (p<1e-4); wave 1 +0.213 (p=0.029), wave 1 adjusted +0.221 (p=0.0026); wave 2 adjusted +0.198 (p=0.0045). Fatal +0.223 (p=9.8e-4). WHO grade +0.103 per grade (p=2.7e-7). COVID +0.105 (p=0.0041). Dexamethasone-type monocytes are a separate observed state: 11.9% of monocytes in wave-2 severe/critical samples vs 1.1% in COVID-.",
+    "beyond_bulk": "No for the generated cells. Bulk CD163 already separates severity (bulk-only donor-level p=0.0066) and fatal outcome (p<1e-4). The generated fraction does not add to the bulk baseline (LR p 0.41-0.80) or to the extended baseline (0.46-0.88, severity). By contrast, the observed single-cell CD163+ fraction does add to the extended bulk baseline for severity (LR p=0.0043; AUC 0.90 to 0.945). This is real single-cell information that the generator does not recover.",
+    "novelty": "Known state. HLA-DRlo CD163hi monocytes in early severe COVID: Schulte-Schrepping 2020 (p.1427). FKBP5/CD163 classical-monocyte module with severity: COMBAT 2022 (p.922, Fig 3H). CD163 up with severity in ESKD monocytes: Stephenson 2025 (Fig 3C, p.6). Specific to this cohort: the association holds in wave-1 observed cells, sampled before dexamethasone was standard care, so it is distinct from Stephenson 2025's glucocorticoid-induced 'dex. monos' (wave 2 only, p.10-11). The generator does not produce those.",
+    "strength": "weak",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s07_rules.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_results.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s15_finding_numbers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08b_increment_extended.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s08_bulk_score_assoc.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s10_dexmono.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s10_dexmono_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s10_gene_detection.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s13_rule_markers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s14_training_donors.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a00a66f13424358ff",
+     "verdict": "qualified",
+     "recomputed": "All numbers below come from my own code, run on the raw cells.parquet and markers.npz files, CPU only. T_LO = 23.086 CP10k, from 9,735 CD14 cells in COVID− samples. Thinning factors by draw: 0.813, 0.691, 0.629, 1.000, 0.676. I used 5 thinning seeds of my own.\n\nBULK-ONLY RESULTS (donor-level Mann-Whitney, values for d0..d4)\n- Severity, 28 vs 27–30 donors: d0 +0.043 (0.187 vs 0.144, p=0.026); d1 +0.056 (p=0.0064); d2 +0.029 (p=0.088); d3 +0.126 (p=0.0007); d4 +0.119 (p=0.0002). This matches the claim exactly.\n- Fatal: +0.059 to +0.195, p≤0.0038 in 5/5 draws.\n- WHO grade slope: +0.019 to +0.078 per grade, p≤0.0028 in 5/5.\n- COVID: +0.045 to +0.140, p≤0.012.\n- CD163+ HLA-II-low subset, severity: +0.023 to +0.085, p 0.0003–0.046 in 5/5.\n\nCONFOUNDER CONTROLS (severity p-values, d0..d4)\n- Adjusted for age, sex, wave and days (donor-clustered OLS): 0.008 / <1e-4 / 0.004 / <1e-5 / <1e-5.\n- sex_flag and training-donor samples removed: 0.030 / 0.014 / 0.16 / 0.001 / 0.0001.\n- Wave 1 only: 0.22 / 0.031 / 0.42 / 0.010 / 0.0016.\n- First sample per donor: 0.004 / 0.0004 / 0.020 / 0.0003 / <1e-4.\n- Label confidence ≥0.9: 0.031 / 0.020 / 0.10 / 0.0005 / 0.0003. Requiring both labels to agree, or a minimum of 10 or 50 parent cells, changes nothing.\n- Depth-matched detection (expected CD163 detection after thinning each cell to 600 UMI): differences +0.019 / +0.033 / +0.022 / +0.060 / +0.076, p 0.088 / 0.018 / 0.059 / 0.011 / 0.0015. All 5 draws keep the same sign.\n- Adjusted for the generated CD14 share: p 0.046 / 0.027 / 0.12 / 0.0056 / 0.34. In d4 the coefficient falls from 0.119 to 0.024.\n- WHO grade, within-donor slope: p 0.079 / 0.006 / 0.27 / 0.028 / 0.077.\n- Fatal, adjusted for severity: p 0.13 / 0.038 / 0.28 / 0.13 / 0.23.\n\nCOMPOSITION ARTEFACT\n- In bulk-only samples the generator makes about twice as many CD14 cells for severe samples (CD14 share 0.14 vs 0.075 in d0; p≤0.007 in every draw).\n- Observed paired cells show no such difference (0.072 vs 0.065, p=0.58).\n- In d4, among severe donors, the CD163+ fraction tracks the generated CD14 share (rho 0.76).\n\nCALIBRATION (paired test samples, COVID+, generated vs observed thinned, across 5 seeds)\n- Level ratio: d0 0.508–0.525; d1 0.535–0.557; d2 0.534–0.547; d3 0.673; d4 1.077–1.113.\n- Spearman: 0.50–0.65.\n- Severity contrast, generated vs observed: +0.096 / +0.092 / +0.098 / +0.144 / +0.143 vs +0.204–0.219 / +0.180–0.214 / +0.196–0.207 / +0.238 / +0.160–0.183. Sign agrees in 5/5; magnitude is 0.43–0.90×.\n- The gate passes in 5/5 draws for every seed.\n- With per-cell depth matching instead of median thinning, the ratio becomes 0.437 / 0.496 / 0.494 / 0.671 / 1.147, and the contrast is 0.34 / 0.40 / 0.50 / 0.60 / 0.99×. The gate then passes only in d3 and d4.\n- Restricted to wave 1, the gate passes in d1, d3 and d4; d0 and d2 pass for only 1 of 5 seeds.\n- Fatal among severe: the generated contrast is only 0.05–0.47× of observed.\n\nOBSERVED TRUTH (native cells)\n- Severity: +0.217 (13 vs 21 donors), p=2.6e-4.\n- Adjusted for clinical covariates: +0.206, p=2.7e-5.\n- Depth-matched: +0.126, p=0.0009.\n- Wave 1: +0.213 (6 vs 13 donors), p=0.029. Leaving out one severe donor at a time gives p 0.010–0.075. Dropping the wave-1 donors that carry dex-rule cells (C21, C123, C124, C23) gives +0.129, p=0.23.\n- Fatal: +0.223, p=0.001. Fatal among severe: +0.095, p=0.44 (8 vs 5 donors).\n- WHO grade: +0.136 per grade, p=7.6e-6 (within-donor slope +0.103, p=0.016).\n- COVID: +0.105, p=0.0041.\n\nBULK BASELINE\n- Bulk CD163 alone separates severity (p=0.0066) and fatal outcome (p<1e-4).\n- Does the generated fraction add? Likelihood-ratio (LR) p-values, d0..d4:\n  - to the core baseline (CD163 + HLA2 + monocyte genes + ISG10): 0.79 / 0.54 / 0.65 / 0.53 / 0.44;\n  - to the extended baseline: 0.87 / 0.48 / 0.63 / 0.49 / 0.52;\n  - to bulk CD163 alone: 0.20 / 0.09 / 0.51 / 0.019 / 0.021.\n  - Leave-one-donor-out (LOO) AUC never improves.\n- The observed fraction does add: LR p 0.011 (CD163 alone), 0.0028 (core), 0.0034 (extended); LOO AUC rises from 0.82 to 0.88.\n- The observed depth-matched fraction does not: LR p 0.088 / 0.27 / 0.135, and LOO AUC does not improve.\n\nMARKERS (CD163+ HLA-II-low vs other CD14 monocytes, logFC)\n- Observed: IL1R2 +1.97, THBS1 +1.17, DDIT4 +1.15, ZBTB16 +1.13, FKBP5 +0.94, MCEMP1 +0.78, RNASE2 +0.68, PLBD1 +0.51.\n- Generated bulk-only: IL1R2 +0.22 to +0.69, MCEMP1 +0.07 to +0.46, ZBTB16 about 0, FKBP5 +0.29 to +0.64, THBS1 +0.32 to +1.04, RNASE2 +0.76 to +0.84.\n- Spearman of logFC, generated vs observed, over 823 expressed genes: 0.38–0.54 per draw, 0.57 pooled.\n\nDEXAMETHASONE-TYPE RULE (CD163 plus ≥1 of ADAMTS2/VSIG4/SLC1A3/MFGE8/MRC1)\n- Generated vs observed ratio: 0.017–0.090.\n- Observed share of monocytes by stratum: COVID− 1.1%; wave-2 severe 11.9%; wave-1 severe 16.4% (5 samples, from donors C123, C21 and C40).\n- In wave-1 severe CD14 pseudobulk, FKBP5 (p=0.046) and ADAMTS2 (p=0.033) are up.\n\nOTHER\n- ISG overlap: of CD163+ HLA-II-low cells, 43–57% have ≥3 ISGs detected in generated cells and 51% in observed cells, not 64%.\n- Shared sampler noise: at the same cell slot, CD163 detection agrees between draws with odds ratio 4.2–5.8, against 0.9–1.5 at a shifted slot.",
+     "issues": [
+      "The calibration is weak in draws 0–2. Generated cells reach only 0.51–0.56× the observed level and 0.43–0.51× the observed severity contrast, so the gate passes only at its 0.5 bound. With exact per-cell depth matching instead of median thinning, the ratio drops to 0.44–0.50 and the gate fails in d0–d2. Only d3 and d4 pass the gate under both depth methods; restricted to wave 1, the gate passes in d1, d3 and d4.",
+      "Draw 4's effect cannot be separated from a composition artefact. The generator makes about twice as many CD14 cells for severe samples, which the observed paired cells do not show (p=0.58). In d4 the CD163+ fraction tracks that share (rho 0.76 among severe donors), and adjusting for it removes the d4 severity effect (+0.119 falls to +0.024, p=0.34). d4 also carries the imprint of its training donor C33: its level is 1.08–1.11× observed.",
+      "Per-cell depth is a partial confounder. The median library of generated parent cells differs by severity in d2, d3 and d4. With depth-matched detection all effects stay positive but shrink: d0 p=0.088, d2 p=0.059. With donor-level clinical plus technical covariates, d0 p=0.18 and d4 p=0.18.",
+      "The result holds robustly only in d1 and d3. d2 is not significant (p=0.088). d0 is fragile: wave 1 only p=0.22; depth-matched plus CD14-share adjustment p=0.20. Agreement across draws is inflated by shared sampler noise: CD163 detection agrees at the same cell slot with odds ratio 4–6.",
+      "The fatal-outcome association does not add anything beyond severity. All 12 fatal bulk-only donors are severe. Fatal adjusted for severity is significant in only 1 of 5 draws (p 0.04–0.28). In observed cells, fatal vs non-fatal among severe donors is +0.095, p=0.44. The generator reproduces the paired fatal-among-severe contrast at only 0.05–0.47×.",
+      "The observed wave-1 evidence behind 'not dexamethasone-induced' is fragile. It rests on 6 severe vs 13 mild donors (p=0.029), and removing any one of C113, C123, C21 or C65 gives p=0.075. The claim's own dex-mono rule finds 16.4% dex-type monocytes in wave-1 severe samples, more than in wave-2 severe samples (11.9%). Those wave-1 cells come from C123 and C21, the donors with the highest CD163+ fractions. Wave-1 severe CD14 pseudobulk also shows FKBP5 and ADAMTS2 up. So 'distinct from dex monos' is not supported, and a glucocorticoid-like component in wave 1 cannot be excluded. The severity effect does survive adjustment for a 4-gene glucocorticoid score (p=0.024), so 'not only dexamethasone' remains plausible.",
+      "'The observed single-cell fraction adds beyond bulk' holds for raw CD163 detection (LR p 0.003–0.011; LOO AUC 0.82 to 0.88). It does not hold for depth-matched detection (LR p 0.09–0.27; no LOO gain). Monocytes in observed severe samples are deeper (median +411 UMI).",
+      "'Adds nothing beyond bulk CD163 expression' is true against the 4–6 gene bulk baselines. Against bulk CD163 alone, the generated fraction does add in d3 and d4 (LR p 0.019 and 0.021).",
+      "Several quoted numbers or descriptors are off: (1) The observed WHO-grade slope is +0.136 per grade (p=7.6e-6), not +0.103 (p=2.7e-7); 0.103 is the within-donor slope (p=0.016). (2) ISG overlap is 43–57% for ≥3 ISGs, not 64%. (3) THBS1 is partly reproduced (logFC +0.32 to +1.04 vs +1.17 observed); the clearly missing arm is IL1R2, MCEMP1 and ZBTB16. (4) 'Mostly HLA-II-low' holds for the increase with severity (53–94% of it is HLA-II-low), but at the level only about 50% (generated) and 31% (observed) of CD163+ cells are HLA-II-low.",
+      "Archive maintenance under POLICY.md was not run by this verifier; writes were restricted to the lens directory, so this verifier produces no archive delta. Everything ran CPU only under systemd-run with MemoryMax=6G, and no GPU work was started."
+     ],
+     "reasoning": "I recomputed every per-sample fraction independently from cells.parquet and markers.npz. The bulk-only severity, fatal, WHO-grade and COVID effects and the paired calibration numbers match the claim almost exactly. The observed paired cells clearly show the association. It survives adjustment for age, sex, wave and days since swab, removal of sex_flag and training-donor samples, donor aggregation and first-sample-per-donor analysis, the label-confidence variants, and the minimum-parent-cell checks.\n\nThe claim still overstates its trust layer:\n- Draws 0–2 reproduce only about 0.5× the level and contrast, and fail the 0.5 ratio gate under exact per-cell depth matching.\n- Draw 4's effect is carried by a generated CD14-share composition artefact that the observed cells do not have.\n- d2 is not significant and d0 is fragile. Under every control, the robust carriers are d1 and d3. d4 is calibrated but confounded by the composition artefact; d0 and d2 are borderline.\n- The fatal association is the severity association again.\n\nThe argument that this is not dexamethasone rests on 6 wave-1 severe donors and is sensitive to removing any one of them. Its own dex-mono rule is as frequent in wave-1 severe samples as in wave 2. The observed increment over bulk disappears with depth-matched detection.\n\nThe negative finding, that the generated fraction adds nothing beyond a small bulk baseline, is confirmed.\n\nNet: this is a reproducible but weakly calibrated, partly confounded echo of a real observed CD163+ monocyte severity association. It is supported in d1 and d3, so the correct verdict is qualified, not confirmed or refuted.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/run.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v00_explore.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v01_fracs.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v02_assoc.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v02b_tech_adjust.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v04_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v05_markers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v06_wave1_gc_noise.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/v07_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v07_summary_by_draw.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v01_fracs.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v01_thresholds.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v01_lib_quantiles.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v02_assoc.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v02b_tech_adjust.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v04_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v05_marker_logfc.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v05_marker_concordance.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v06_gc_assoc.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v06_gc_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v06_shared_noise.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/tables/v00_trained_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_recompute/logs"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a8b9f76f28940b632",
+     "verdict": "qualified",
+     "recomputed": "I rebuilt the analysis from work/imperial_eskd with independent code (v01-v05). I did not import any of the candidate's modules. Observed cells were thinned to each draw's generated CD14-monocyte median library; I used seed 0 and checked 5 seeds for spread.\n\nCALIBRATION, CD14 CD163+ share (detection >= 1 count), COVID+ paired test samples, draws d0-d4:\n- Ratio 0.52/0.55/0.53/0.67/1.09; across seeds d0-d2 range 0.51-0.56.\n- Spearman 0.52/0.61/0.61/0.62/0.64.\n- Severity contrast, generated vs observed: +0.096/+0.092/+0.098/+0.144/+0.143 vs +0.217/+0.189/+0.195/+0.238/+0.173. Contrast ratio 0.44/0.49/0.50/0.61/0.83, same sign 5/5.\n- WHO-grade slope, generated vs observed: 0.058/0.041/0.041/0.102/0.099 vs 0.130/0.124/0.142/0.149/0.126.\n- Fatal and COVID contrasts have the same sign in 5/5 draws.\n\nCALIBRATION, harder variants:\n- Wave-1 COVID+ test samples only (126 of the 143 bulk-only COVID+ samples are wave 1): ratio 0.50/0.57/0.48/0.64/0.97. Across seeds d0 is 0.487-0.502 and d2 is 0.477-0.499.\n- Depth-robust per-cell level (CD163 CP10k in CD14 monocytes): ratio 0.39/0.44/0.44/0.59/1.16; severity contrast ratio 0.25/0.29/0.40/0.47/0.88.\n- Detection >= 2 counts: ratio 0.43/0.48/0.47/0.61/1.45.\n- So draws 0-2 fail the 0.5 ratio gate under the depth-robust and >= 2-count definitions. Draws 3-4 pass under every variant.\n- CD14 pseudobulk level ratios, generated vs observed: IL1R2 0.02-0.27, MCEMP1 0.23-0.27, THBS1 0.04-0.25, ADAMTS2 0.05-0.11, VSIG4 0.01-0.06. FKBP5 is over-produced at 1.67-2.50x.\n- calibration.csv 'MS1 monocyte (IL1R2+)': ratio 0.04-0.37.\n\nBULK-ONLY, per draw (reproduces the candidate):\n- Severity: +0.043 (p=0.026), +0.056 (0.0064), +0.029 (0.088), +0.127 (0.0007), +0.119 (0.0002).\n- Wave 1 only: p 0.22 / 0.031 / 0.42 / 0.010 / 0.0016.\n- The 31 COVID+ donors with no single-cell sample at all (all wave 1): severity +0.005 (0.86), +0.038 (0.25), -0.009 (0.46), +0.089 (0.069), +0.092 (0.034). Fatal is significant only in d4 (p=0.023). The WHO-grade slope is significant in d1/d3/d4 (p<=0.0004).\n- Bulk-only donors that also have paired samples: severity p 0.0019-0.015 in all 5 draws.\n\nOBSERVED native cells (reproduces the candidate):\n- Severity +0.217 (p=2.6e-4, 13 vs 21 donors).\n- Wave 1: +0.213 (p=0.029). Leave-one-donor-out p ranges 0.010-0.075; dropping any one of C113, C123, C21 or C65 gives p=0.075.\n- Wave 2: +0.226 (p=0.014). Fatal: +0.223 (p=9.8e-4).\n- WHO grade: +0.136 per grade (p=1e-5). The candidate quotes +0.103 (p=2.7e-7), which is not in its own s07/s15 tables.\n- CD163+ HLA-II-low subset, wave 1: severity p=0.37.\n\nWAVE 1, glucocorticoid signal in observed cells:\n- Across donors, the CD163+ share tracks CD14-monocyte FKBP5 (rho 0.71) and ADAMTS2 (rho 0.59).\n- The wave-1 severity coefficient falls from 0.213 (p=0.024) to 0.110 (p=0.088) after adjusting for FKBP5. In wave 2 it falls to 0.005 (p=0.90).\n- Wave-1 severe vs mild CD14 pseudobulk: ADAMTS2 +2.89 log2 (p=0.026), FKBP5 +0.95 log2 (p=0.022).\n- Wave-1 severe donors C21, C123 and C65 have FKBP5 408-654 CPM against a COVID- 90th percentile of 234.\n\nHLA-II-LOW AND STATE COHERENCE:\n- Observed, pooled: 37% of CD163+ CD14 monocytes are HLA-II-low, against 38.5% of all CD14 monocytes. Within sample, the odds ratio for HLA-II-low is 0.71 (subsample) and 0.79 (all cells).\n- Generated: 49-62% are HLA-II-low, against a generated base rate of 54-64%.\n- Within-sample co-detection with CD163 (Mantel-Haenszel odds ratio, strata = sample x library tercile; null median 1.11), observed: FKBP5 1.25 (p=0.08), RNASE2 1.00, PLBD1 0.98, DDIT4 0.97, IL1R2 1.36 (all cells 1.19).\n- Same test in generated cells: FKBP5 1.41, RNASE2 1.58, STAB1 1.85, THBS1 1.75 (all p<0.001).\n- Within-sample CD163 dispersion: observed 1.07-1.13, generated 1.31-1.43.\n\nBEYOND BULK:\n- Observed fraction added to the extended bulk baseline, severity: LR p=0.0043 asymptotic, 0.021 by permutation. Fatal: p=0.10 (permutation 0.25).\n- Generated fraction: LR p 0.41-0.98, from the candidate's s08/s08b; I checked the code.\n- Partial Spearman of generated vs observed share, given bulk CD163: 0.39-0.51.\n\nISG+ share of CD163+ HLA-low cells: observed 57% (>=3 ISGs) or 47% (>=4); generated 42-60%. The candidate's 64% does not reproduce.",
+     "issues": [
+      "'Mostly HLA-II-low' is refuted as a property of the state. Observed CD163+ CD14 monocytes are HLA-II-low at the same rate as all CD14 monocytes (37% vs 38.5%), and within sample they are less likely to be HLA-low (odds ratio 0.71-0.79). The generated 49-62% simply reflects a generator-wide HLA-II deficit: 54-64% of all generated CD14 monocytes fall below the observed threshold.",
+      "The 'FKBP5+, RNASE2+, PLBD1+' state is not supported by observed cells. Within sample, observed co-detection with CD163 is null for RNASE2 (1.00), PLBD1 (0.98) and DDIT4 (0.97), and weak for FKBP5 (1.25, p=0.08). The s13 marker list (s13_rule_markers.csv), which pools cells across samples, reflects differences between samples. In observed cells CD163 detection is close to Poisson (dispersion about 1.1), so the share largely reads out per-sample CD163 level. Generated cells instead show a within-sample CD163 module (RNASE2 1.58, STAB1 1.85, THBS1 1.75) that observed cells do not have.",
+      "'Not only dexamethasone-induced' is overstated. Wave-1 severe CD14 monocytes carry a glucocorticoid-response programme: ADAMTS2 +2.9 log2 (p=0.026), FKBP5 +0.95 log2 (p=0.022). FKBP5 explains about half of the wave-1 severity effect (0.213 to 0.110, p=0.088). The candidate's own s10 table puts the dex-like rule at 16.4% in wave-1 severe observed samples (C123 7-19%, C21 36%), above the 11.9% in wave 2, and the claim omits this. Endogenous stress cortisol or undocumented steroid exposure cannot be excluded, so separation from glucocorticoid ('dex.') monocytes is not shown.",
+      "The CD163+ HLA-II-low subset has no wave-1 observed support (p=0.37; adjusted p=0.066).",
+      "The calibration gate for draws 0-2 is fragile. Their ratio of 0.51-0.56 sits at the 0.5 floor. They fail with the depth-robust CP10k level (0.39-0.44) and with >=2-count detection (0.43-0.48), and d0/d2 fail in the wave-1 population (0.48-0.50). Only draws 3-4 pass robustly; d1 is marginal. Contrast magnitude is 0.44-0.83x.",
+      "The bulk-only effect sits mostly in donors that also have paired samples, including all 10 wave-2 donors. Among the 31 donors with no single-cell data, severity reaches p<0.05 only in d4 (p=0.034), d3 is borderline (0.069), and d2 has the opposite sign. Fatal reaches p<0.05 only in d4. The WHO-grade slope holds in d1/d3/d4.",
+      "Calibration transfer fails for the bulk-only wave-2 severe donors (n=5, all fatal). Their bulk FKBP5, ADAMTS2 and IL1R2 are 1.4-2.8 log2 above the paired wave-2 severe donors (p 0.005-0.018), so they lie outside the calibrated range. They feed the fatal contrast.",
+      "The draws that carry the result (3 and 4) were trained on C108 and C33. Both are wave-2 severe, fatal, steroid-era donors with high dex-like monocyte shares (C33 16-44%, C108 7-21%). Draw-4 bulk-only cells co-detect ADAMTS2 with CD163 within sample (odds ratio 5.3, p=0.0035). The strongest generated signal therefore partly imprints a glucocorticoid-exposed training donor.",
+      "The finding is not novel for this cohort. Stephenson 2025, printed p.5 (PDF p.6; Fig 3C on p.6), reports CD163 upregulated in monocytes at severe/critical sampling, from a model over both cohorts with a centre term. Printed p.10: dex. monos are CD163+ ADAMTS2+ and HLA-DRA/DRB5-low. Schulte-Schrepping 2020 p.1427 describes HLA-DRlo CD163hi monocytes early in severe disease. COMBAT 2022 p.922 reports an FKBP5.CD163 cMono module (Fig 3H) among modules associated with severe disease. Gisby 2022 p.2: 8 of 17 wave-2 patients received glucocorticoids.",
+      "Nothing beyond bulk. The generated share never adds to the bulk baselines (LR p 0.41-0.98). The observed share adds for severity only, at permutation p=0.021 (the candidate quotes the asymptotic 0.0043, with 13 events and 8 parameters); it does not add for fatal outcome.",
+      "Numbers that do not trace: observed WHO grade '+0.103 per grade (p=2.7e-7)' is not in s07/s15, which give +0.136 (p=8e-6); '64% ISG+' does not reproduce (47-57% observed, 42-60% generated).",
+      "Agreement across draws is inflated by shared sampler noise (seed 20260927), so 5/5 same sign is not five independent replications."
+     ],
+     "reasoning": "Verdict: qualified. The core numbers reproduce exactly, but several sub-claims fail.\n\nCalibration supports the direction and only partial magnitude, with draws 0-2 at or below the gate. On COVID+ paired test samples the sign agrees for severity, fatal outcome, WHO grade and COVID in 5/5 draws, Spearman is 0.5-0.65 and the contrast is 0.44-0.83x. The level ratio, however, sits at the 0.5 floor in draws 0-2. Those draws fail when the same biology is measured depth-robustly (CP10k 0.39-0.44) or with a 2-count threshold. Draws 0 and 2 also fail in the wave-1 population, which is the population of the bulk-only samples. The associated IL1R2/MCEMP1/THBS1/ADAMTS2/VSIG4 arm is generated at 0.01-0.27x. Draws 3 and 4 carry the result robustly, and draw 1 only marginally. Draws 3-4 were trained on CD163-rich, steroid-era wave-2 donors (C108, C33).\n\nThe observed paired cells strongly confirm a sample-level association between the CD163+ share and severity, fatal outcome and WHO grade. Observed cells contradict the state description in three ways:\n- CD163+ cells are not HLA-II-low-enriched.\n- RNASE2, PLBD1 and DDIT4 do not co-occur with CD163 within a sample; FKBP5 only weakly. The marker list comes from differences between samples.\n- The wave-1 association, the basis for 'not dexamethasone-induced', co-varies with an FKBP5/ADAMTS2 glucocorticoid programme. FKBP5 absorbs about half of it, and the candidate's own s10 table shows dex-like monocytes in wave-1 severe donors.\n\nBiology is known. Stephenson 2025 already reports monocyte CD163 rising with severity in this cohort, Schulte-Schrepping 2020 describes HLA-DRlo CD163hi monocytes, and COMBAT 2022 reports an FKBP5/CD163 cMono module.\n\nThe generated share adds nothing beyond bulk. In the 31 donors that have no single-cell data at all, the bulk-only severity effect reaches p<0.05 only in draw 4.\n\nOverall: the generator reproduces, at roughly half strength, a known, bulk-derivable CD163/glucocorticoid-linked monocyte shift that is largely a change in level. It does not show a new or glucocorticoid-independent cell state.\n\nProcess: CPU only, every run under systemd-run with MemoryMax=6G, nothing written outside my dir. The coordinator owns any archive update, so this verifier makes no archive delta.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_calib/tables/v04_w1_gc_adjust.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_calib/tables/v04_bulkonly_newdonors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_calib/tables/v05_newdonor_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-2_calib/tables/v05_newdonor_gen.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s10_dexmono_samples.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s07_rule_results.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/features/imperial_eskd/calibration.csv",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/COMBAT2022_Blood_atlas_COVID19_severity.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/SchulteSchrepping2020_Severe_COVID19_dysregulated_myeloid.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-LONG-3",
+   "lens": "imp_longitudinal",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 14,
+    "theme": "interferon-linked label (NK)",
+    "synthesis_strength": "weak: an IFN-clock decline of a mislabelled NK state; size calibrated only in draws 1-2; nothing beyond bulk",
+    "reportable_statement": "The share of generated NK cells labelled 'Proliferating NK' falls over the first three weeks in bulk-only COVID+ samples (-0.073, -0.039, -0.056, -0.089, -0.064 logit/day), as observed. The label mostly marks ISG-high, non-cycling NK cells (only 7-9% carry a cycling transcript); truly cycling NK cells do not fall. This is the IFN-stimulated NK decline already reported for this cohort, fully explained by the bulk ISG score.",
+    "carrying_draws": {
+     "direction": "0-4",
+     "size, all waves": "1,2 (strict gate: 2)",
+     "wave 1": "0,1,2",
+     "draws 3-4": "too steep (~2.5x), explained by rising generated depth"
+    },
+    "honesty": {
+     "calibration": "calibration.csv ratio 0.40-1.04, Spearman 0.45-0.59, COVID and severity signs 5/5 (COVID PASS 4/5 + DIR; wave-1 COVID PASS 5/5, synthesis i01). Time slope on the same COVID+ samples: generated -0.036 to -0.116 vs observed -0.043 to -0.051; draws 0, 3, 4 significantly too steep (p <= 3e-4). The generator's ISG dependence is 1.5-2.7x the observed one.",
+     "replication": "5/5 negative; survives age/sex/fatal/WHO, training donors, sex flag, label confidence, arcsine, fixed effects, random slopes. Carried by the 31 no-single-cell donors: bulk-only visits of donors with paired visits show no decline in any draw (-0.050 to +0.020; interaction p < 0.03 in draws 1-4). Draw 1 per-donor median slope ~0 (p 0.25); draw 2 bootstrap CI crosses 0.",
+     "observed": "Observed -0.045/day (p 2e-7); wave 1 -0.037 (p 0.009) with no week-1 peak (0.026, 0.027, 0.012). Labelled ISG-high non-cycling cells carry the decline (-0.20 logit/day; 64-85% of the drop); cycling NK flat (0.58/0.58/0.61% of cells; -0.007, p 0.55).",
+     "beyond_bulk": "None: bulk ISG removes the slope (observed -0.013, p 0.32; generated -0.010 to +0.008) and tracks the observed share better (0.54-0.63 vs 0.43-0.54); a 3-score linear bulk model trained on the same five donors beats the generated cells in 4/5 draws."
+    },
+    "verdict_reasons": [
+     "Biology misread: 'proliferating' NK cells are mostly ISG-high, non-cycling NK cells.",
+     "Size calibrated only in draws 1-2; draws 0, 3, 4 too steep.",
+     "The decline is carried by donors without single cells.",
+     "'Most common at days 0-7' holds only in wave 2.",
+     "Already reported (Stephenson Fig. 4A)."
+    ],
+    "refuted_subclaims": [
+     "proliferating NK cells fall",
+     "in every draw",
+     "peak in days 0-7 (wave 1)"
+    ],
+    "new_vs_cohort_papers": "Known: Stephenson 2025 Fig. 4A (PDF p.8) and text (PDF p.9): IFN-stimulated NK cells raised in weeks 1-2 then reduced.",
+    "next_steps": [
+     "Rename the feature 'IFN-stimulated NK (label)'; use marker-defined cycling NK for proliferation.",
+     "Depth-match generated libraries over time before trajectory claims."
+    ],
+    "key_sources": [
+     "verify/IMP-LONG-3_recompute/v11_draw_gates.csv",
+     "verify/IMP-LONG-3_recompute/v04_depth_adjusted.csv",
+     "verify/IMP-LONG-3_recompute/v07_scdonors.csv",
+     "verify/IMP-LONG-3_recompute/v09_components.csv",
+     "verify/IMP-LONG-3_calib/v01_time_calibration.csv",
+     "verify/IMP-LONG-3_calib/v05_label_markers.csv",
+     "verify/IMP-LONG-3_calib/v05c_native_slopes.csv",
+     "verify/IMP-LONG-3_calib/v07_decline_attribution.csv",
+     "verify/IMP-LONG-3_calib/v10_bulk_baseline.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-LONG-3",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only donors, generated proliferating NK cells are most common at days 0-7 and fall over 3 weeks (-0.039 to -0.089 logit/day in every draw, also in donors without single cells), as in observed paired cells (0.063 -> 0.036 -> 0.025 of all cells). The generated decline is steeper than observed (1.2-2.2x in wave 1). A bulk cycling-gene score does not capture it, but the bulk ISG score explains it fully, in both observed and generated cells.",
+    "kind": "trajectory",
+    "feature": "l2 | Proliferating NK cell",
+    "comparison": "Days since first positive swab (0-21 d). COVID+ samples. Bulk-only generated cells per draw vs observed paired cells, all waves and wave 1.",
+    "effect_bulkonly": "Raw window means (share of all cells), d0-7/d8-14/d15-21/>21:\n- d0: 0.041/0.026/0.019/0.016\n- d1: 0.026/0.015/0.015/0.012\n- d2: 0.039/0.021/0.023/0.023\n- d3: 0.048/0.029/0.016/0.010\n- d4: 0.052/0.034/0.022/0.020\n\nSlopes (logit/day):\n- Severity-adjusted, all bulk-only: -0.073, -0.039, -0.056, -0.089, -0.064.\n- Wave 1: -0.076, -0.046, -0.058, -0.085, -0.066.\n- No-single-cell donors: -0.082, -0.063, -0.091, -0.104, -0.084.",
+    "stats": "Model: MixedLM, logit ~ days + severity_peak + wave + (1|donor).\n- Per-draw p: 7e-4 to 3e-10. All-wave BH q (50 features): <= 0.013.\n- Wave-1 BH q: 0.007 to 5e-9.\n- Within-donor FE slope: -0.033 (p 0.067, d1); -0.067 to -0.112 (p <= 0.003, other draws).",
+    "draws_same_sign": "5/5. Shared seed, so not independent.",
+    "paired_calibration": "calibration.csv: ratio 0.40-1.04, Spearman 0.45-0.59; COVID+/- and severity signs 5/5.\n\nCOVID+ test samples (s03):\n- Ratio 0.39-1.03, Spearman 0.43-0.54, within-donor tracking 0.30-0.66.\n- Slope: generated -0.038 to -0.116 vs observed -0.044 to -0.051.\n- TIME_PASS in draws 0 and 2 only; direction agrees in 0-4.\n\nWave 1 (s16): slope ratio 0.84-2.07; W1_TIME_PASS in draws 0,1,2.",
+    "paired_observed_concordance": "Yes for direction and timing; the generated slope is too steep in some draws.\n- Observed: -0.045/day (q 2e-6); wave 1 -0.038 (p 0.008); wave 2 -0.052 (wave x day p 0.38).\n- Within-donor FE in observed cells: -0.043.\n- The observed quadratic peak near day 3 is not resolved; the generated decline is monotone from week 1.",
+    "beyond_bulk": "It goes beyond a cycling-gene score, but not beyond the bulk IFN clock.\n- Bulk cycling score (MKI67, TOP2A, TYMS, CENPF, UBE2C, BIRC5): Spearman with observed 0.06 vs 0.51 for generated cells. It even rises in paired samples (+0.042 SD/day).\n- The share-based bulk proxy matches the generated cells (Spearman 0.52 vs 0.51; within-donor 0.43 vs 0.57).\n- Adjusting for the bulk ISG score removes the slope in observed cells (-0.013, p 0.32; wave 1 -0.019, p 0.35) and in generated cells (-0.018 to +0.008, p >= 0.15).",
+    "novelty": "Not reported as a trajectory in either cohort paper (text search). Stephenson 2025 PDF p.9 notes IFN-stimulated NK cells persisting into weeks 2-3. Literature outside the cohort papers was not checked.",
+    "strength": "weak",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s02_observed_truth.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s03_temporal_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s04_bulkonly_traj.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_tracking.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s09_shapes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s11_isg_adjusted.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s13_within_donor.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s17_wave1_beyond_bulk.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s21_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "af33920010b541d28",
+     "verdict": "qualified",
+     "recomputed": "I wrote my own code from cells.parquet and markers.npz. The per-sample PNK counts match composition.parquet exactly (1692/1692 rows). Population: COVID+, days 0-21. Model: LMM on the empirical logit, y ~ days + sev + w2 with a donor random intercept.\n\n**Reproduction**\n- Bulk-only slopes (ML), draws 0-4: -0.0727, -0.0389, -0.0557, -0.0893, -0.0642 (p 2.9e-10, 1.3e-4, 7.2e-4, 7.2e-8, 2.4e-8).\n- BH q over 50 features: 5e-9 to 0.009 (0.013 with the discovery's days+w2 model).\n- Wave 1 (110 samples / 47 donors): -0.076, -0.046, -0.058, -0.085, -0.066.\n- Donors with no single cells (77/31, all wave 1): -0.082, -0.063, -0.091, -0.104, -0.084.\n- Window means d0-7/d8-14/d15-21/>21 match the claim; for example draw 0: 0.041/0.026/0.019/0.016.\n- Observed paired cells: 0.063 -> 0.036 -> 0.025, slope -0.0449 (p 1.7e-7), wave 1 -0.038, wave 2 -0.052, wave x day p 0.39, within-donor FE -0.043.\n\n**Confounder and robustness controls (bulk-only, per draw)**\n- Age + sex + fatal + WHO-at-sample: -0.077, -0.035, -0.053, -0.092, -0.070.\n- Dropping training donors and the one sex_flag sample: -0.082, -0.046, -0.054, -0.099, -0.069.\n- Only cells with L2 confidence >= 0.9: -0.079, -0.034, -0.055, -0.091, -0.067.\n- Arcsine transform, within-donor FE, donor random slope and leave-one-donor-out: all negative in 5/5 draws.\n  - FE: -0.088, -0.033 (p 0.029), -0.067, -0.112, -0.079.\n  - Random slope: -0.070, -0.028 (p 0.014), -0.041 (p 0.027), -0.087, -0.060.\n- Weak points:\n  - Draw 1, per-donor slopes: median -0.001, Wilcoxon p 0.25.\n  - Draw 2, donor bootstrap 95% CI: -0.085 to +0.004.\n- Cell number cannot confound in generated sets (always 1,500 cells). In observed cells, adding log n_cells gives -0.044.\n\n**Library depth**\n- Generated T-cell median library rises with days in draws 3 and 4 (+0.018 and +0.015 log/day, p<1e-6).\n- Adjusting for it gives bulk-only slopes of -0.064, -0.038, -0.044, -0.028 (p 0.045), -0.021 (p 0.032).\n- A cell-level, depth-matched test inside the NK gate is still negative in all draws: -0.070, -0.040, -0.052, -0.046, -0.053 (p<1e-9).\n\n**Subsets**\n- Bulk-only visits of donors who also have paired visits (50 samples / 26 donors): -0.050 (p 0.05), +0.008, +0.017, -0.046 (0.16), -0.029 (0.19).\n- Days x no-single-cell-donor interaction p, all waves: 0.21, 4e-4, 1e-3, 0.07, 0.02. Within wave 1: 0.29, 0.004, 3e-4, 0.013, 0.011.\n\n**Trust layer**\n- calibration.csv: ratio 0.76, 0.40, 0.68, 0.91, 1.04; Spearman 0.59, 0.54, 0.55, 0.49, 0.45; COVID+/- and severity signs 5/5.\n- Recomputed on COVID+ test samples within 21 days: ratio 0.39-1.03, Spearman 0.43-0.54, within-donor tracking 0.30-0.66.\n- Same-sample slopes, generated vs observed:\n\n| Draw | Generated | Observed | Generated-minus-observed p |\n|---|---|---|---|\n| 0 | -0.093 | -0.051 | 3e-4 |\n| 1 | -0.058 | -0.044 | 0.15 |\n| 2 | -0.036 | -0.043 | 0.59 |\n| 3 | -0.115 | -0.045 | 3e-7 |\n| 4 | -0.106 | -0.044 | 4e-6 |\n\n- Wave-1 slope ratios: 0.84-2.10, all not significant.\n\n**Bulk scores**\n- Adjusting for the bulk ISG score removes the slope:\n  - observed: -0.0135 (p 0.33);\n  - bulk-only: -0.011 to +0.007 (p >= 0.36).\n- Bulk ISG Spearman with the observed share is 0.58 (same test samples 0.54-0.63), versus 0.43-0.54 for generated cells. Bulk cycling score: 0.08.",
+     "issues": [
+      "The decline is carried by the 31 donors without single cells. Bulk-only visits of donors who also have paired visits show no significant decline in any draw: slopes -0.050 (p 0.05), +0.008, +0.017, -0.046, -0.029. The subset difference holds inside wave 1 (days x no-single-cell p 0.004, 3e-4, 0.013 and 0.011 in draws 1-4). In draws 1 and 2, those visits also depart from the same donors' paired-visit generated trajectory (days x visit-type p 0.03, 0.04). 'Also in donors without single cells' reads as a replication but hides this.",
+      "The size of the decline is not calibrated. On the same paired COVID+ test samples, the generated slope is significantly steeper than observed in draws 0, 3 and 4 (1.8x, 2.6x, 2.4x; p 3e-4, 3e-7, 4e-6). Only draws 1-2 match the observed size. Only draw 2 passes both the level gate (calibration.csv ratio 0.5-2, Spearman >= 0.3) and the slope gate. The quoted 1.2-2.2x in wave 1 compares bulk-only samples with different paired samples; on the same wave-1 samples the ratio is 0.84-2.10, not significant.",
+      "Library depth explains most of the extra steepness in draws 3-4. Generated T-cell library rises with days (+0.018 and +0.015 log/day, p<1e-6). The generated PNK label is strongly depth-dependent: P(PNK | NK cell) is 0.40-0.53 in the lowest library decile and 0.08-0.12 in the top decile (observed: 0.37 to 0.22). Adjusting for T-cell library cuts the bulk-only slopes of draws 3-4 to -0.028 and -0.021 (p 0.045, 0.032). In paired test samples the same adjustment brings them to -0.035 and -0.042, close to the observed -0.044. The depth-matched within-NK decline survives in all draws.",
+      "The 'Proliferating NK cell' label is mostly not cycling. Only 8.9% of observed PNK-labelled cells detect any cycling gene, versus 30.8% of 'Proliferating T cell' cells in the same data (CD56dim NK: 1.4%). 37.7% of PNK-labelled cells are ISG-high (>=4 of 10 ISG genes). The observed decline sits entirely in the ISG-high, non-cycling part (-0.20 logit/day, p<1e-4). The cycling part (-0.018, p 0.19) and the remaining 57% (+0.014) are flat. This is an interferon (IFN) clock in ISG-high NK cells, not a proliferation trajectory; the wording should say so.",
+      "The result adds nothing beyond the bulk. Adjusting for the bulk ISG score removes the slope in observed cells and in every draw (p >= 0.36). The bulk ISG score tracks the observed share at least as well as the generated cells (Spearman 0.54-0.63 vs 0.43-0.54 on the same samples). Generated values add some sample-level information beyond ISG in draws 0, 1 and 3 only. The claim already states this; it limits the finding.",
+      "Calibration is moderate. Draw 1's level ratio is 0.40, which fails a 0.5 floor. Spearman is 0.45-0.59. Within-donor tracking in draw 2 is only 0.30.",
+      "The per-draw evidence in draws 1-2, the two draws whose slope size is calibrated, is the least robust. Draw 1: per-donor slope median -0.001 (Wilcoxon p 0.25, 51% of donors negative); random-slope p 0.014. Draw 2: donor-bootstrap 95% CI -0.085 to +0.004; random-slope p 0.027.",
+      "All draws share sampling seed 20260927, so '5/5 draws' is not five independent replications (the claim acknowledges this).",
+      "Minor: the observed early peak is at days 4-7 (0.070), with days 0-3 lower (0.049). Generated cells do not resolve it."
+     ],
+     "reasoning": "**Verdict: qualified.** The effect reproduces exactly and survives most confounders. It holds only partly, and its calibration is weak.\n\n**What reproduces.** Every quoted number reproduces from my own code on the raw per-cell tables: slopes, p and q values, window means, the observed trajectory, the FE slopes, the cycling-score Spearman and the ISG-adjusted slopes. Standard confounders do not remove the decline:\n- age, sex, fatal outcome, time-varying WHO severity;\n- wave (wave-1-only fits, wave covariate);\n- sex_flag and training-donor exclusion;\n- label confidence >= 0.7 or >= 0.9;\n- logit vs arcsine scale;\n- donor repeated measures: random intercept, random slope, within-donor FE, two-stage, donor bootstrap, leave-one-donor-out.\nCell number cannot confound in generated sets (fixed 1,500 cells), and it changes nothing in observed cells. So the claim is not refuted.\n\n**Why not confirmed.**\n1. The bulk-only decline is concentrated in donors without single cells. It is absent from the bulk-only visits of donors who have paired data, and that subset difference survives inside wave 1.\n2. The 'steeper than observed' part is miscalibrated on the same paired samples in draws 0, 3 and 4. In draws 3-4 it is largely explained by a rise in generated library depth over time, combined with a strongly depth-sensitive PNK label in generated cells.\n3. The label is mainly ISG-high, non-cycling NK cells, and the observed decline is an IFN-response decline. The claim already concedes it is fully explained by the bulk ISG score, so it adds nothing beyond bulk.\n\n**Which draws carry it.**\n- Direction: all 5 draws. Each reproduces the paired COVID+/-, severity and time-slope signs on its test samples.\n- Size: draws 1-2 only. Draw 0 is borderline-too-steep (1.8x, p 3e-4); draws 3-4 are about 2.5x too steep because of depth.\n- Only draw 2 passes both the level and the slope gate, and its bulk-only result is the weakest (bootstrap CI crosses 0; the paired-data donors' bulk-only visits depart from their paired-visit trajectory).\n\n**Defensible restatement.** In bulk-only COVID+ samples, generated cells labelled 'Proliferating NK' (mostly ISG-high NK) fall over the first three weeks, in the direction of the observed decline. This is driven by donors without single cells. Its size is calibrated only in draws 1-2. It is fully explained by the bulk ISG score.\n\n**Process.** CPU only, every run under systemd-run with MemoryMax=6G. No GPU work. Writes only in the verifier dir. This verifier produced no paper-archive change; the coordinator owns the task-end archive step.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v11_draw_gates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v11_numbers.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v01_cells.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v01_cells_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v02_recompute.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v02_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v02_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v03_confounders.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v03_controls.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v03_qc_trends.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v04_depth.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v04_depth_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v04_depth_bins.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v05_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v05_calibration_restricted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v06_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v06_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v06_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v06_incremental.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v06_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v07_scdonors.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v07_scdonors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v08_bh_randslope.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v08_bh.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v08_randslope.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v09_label.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v09_label_benchmark.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v09_components.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v10_extra.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/v10_extra.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/vutil.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_recompute/run.sh"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a4e0cfa50cb0fbc43",
+     "verdict": "qualified",
+     "recomputed": "All values below were recomputed independently from the raw feature, cell and bulk files; nothing was copied from the candidate's tables. Slopes are in logit units per day since the first positive swab, from mixed models with a random intercept per donor (plus wave), on COVID+ samples at 21 days or less.\n\n(1) Calibration\n- calibration.csv, l2 | Proliferating NK cell, draws 0-4:\n  - Level ratio 0.76 / 0.40 / 0.68 / 0.91 / 1.04; Spearman 0.59 / 0.54 / 0.55 / 0.49 / 0.45.\n  - COVID+ minus COVID−: generated +0.027 / +0.011 / +0.018 / +0.031 / +0.027 vs observed +0.036 / +0.035 / +0.029 / +0.031 / +0.028. Same sign in 5/5 draws.\n  - Severe minus mild: generated +0.023 / +0.008 / +0.016 / +0.045 / +0.017 vs observed +0.011 / +0.014 / +0.025 / +0.006 / +0.004. Same sign in 5/5, but 2-8x too large in draws 0, 3 and 4.\n  - Pooled row: ratio 0.76, Spearman 0.53.\n- The time contrast is not in calibration.csv, so I computed it on each draw's COVID+ paired test samples (n 102-104, 32 donors):\n  - Generated slope −0.095 / −0.059 / −0.038 / −0.116 / −0.106 vs observed −0.051 / −0.044 / −0.044 / −0.045 / −0.045. Same sign in 5/5.\n  - Slope ratio 1.87 / 1.34 / 0.86 / 2.59 / 2.37.\n  - Paired test of the slope difference on the same samples (generated minus observed): −0.044 (p 2e-4), −0.015 (p 0.14), +0.008 (p 0.62), −0.070 (p 1e-7), −0.060 (p 4e-6).\n  - Gates: TIME_PASS = level ratio 0.5-2 and Spearman ≥ 0.3, same slope sign, slope ratio 0.5-2, within-donor tracking > 0. \"Strict\" = TIME_PASS plus no significant slope difference on the same samples. TIME_PASS in draws 0 and 2; strict in draw 2 only.\n  - Wave 1 only (n 45-50): slope ratio 0.80-2.05, TIME_PASS and strict in draws 0, 1 and 2; slope-difference p 0.08-0.68.\n- Why the generated slope is too steep: the generated share responds to the bulk ISG score about 1.5-2.7x as strongly as the observed share does (ISG coefficient 0.80 / 0.52 / 0.57 / 0.91 / 0.82 vs observed 0.33-0.38; difference p ≤ 0.016 in every draw).\n\nBulk-only slopes (severity-adjusted)\n- All bulk-only samples: −0.073 / −0.039 / −0.056 / −0.089 / −0.064 (reproduces the candidate).\n- Without training donors and sex_flag samples: −0.081 / −0.046 / −0.053 / −0.098 / −0.068.\n- Donors with no single cells (77 samples, 31 donors): −0.082 / −0.063 / −0.091 / −0.104 / −0.084.\n- Bulk-only visits of donors who also have paired visits (50 samples, 26 donors): −0.045 / +0.009 / +0.020 / −0.040 / −0.028, all p ≥ 0.08.\n- Share of donors whose own slope is negative: 89 / 51 / 60 / 77 / 77 % (sign-test p 4e-6 / 1.0 / 0.31 / 0.002 / 0.002).\n\n(2) Observed paired cells\n- All waves: −0.045 per day (p 2e-7). Within-donor slope −0.043. 71% of donors decline (p 0.029). Leave-one-donor-out range −0.050 to −0.042.\n- Wave 1: −0.037 (p 0.009). Window means 0.026 / 0.027 / 0.012 (days 0-7 / 8-14 / 15-21), so no early peak; finer bins peak at days 7-10.\n- Wave 2: 0.091 / 0.046 / 0.035.\n\n(3) What the label marks (embedding-free marker checks)\n- Truly cycling NK cells (NK cells with ≥1 of MKI67, TOP2A, TYMS, CENPF, UBE2C, BIRC5, at native depth) do not fall over time:\n  - 0.58% / 0.58% / 0.61% of cells; slope −0.007 per day (p 0.55).\n  - MKI67- or TOP2A-positive NK cells: −0.008 (p 0.55).\n- At equal depth (849 UMI), only 7.1% of cells labelled \"Proliferating NK\" carry any cycling transcript, vs 20.2% of \"Proliferating T\" cells, 34.9% of plasma cells and 1.1% of CD56dim NK cells.\n- ISG-high NK cells are labelled \"Proliferating NK\" much more often: 0.36 vs 0.14 for ISG-low NK cells in observed data (generated 0.34 vs 0.19).\n- ISG-high labelled cells carry most of the drop from days 0-7 to days 15-21: 85% of the observed drop (64% in wave 1) and 68-77% in bulk-only draws 0-2. Cycling labelled cells do not decline in any set.\n\n(4) Does it add anything beyond the bulk?\n- The bulk ISG score alone tracks the observed share better than the generated cells in every draw: Spearman 0.57 / 0.59 / 0.63 / 0.56 / 0.54 vs 0.54 / 0.51 / 0.53 / 0.50 / 0.43.\n- A linear model on three bulk scores (ISG, NK abundance, cell cycle), fitted leaving one donor out: Spearman 0.60-0.69.\n- The same linear model fitted on only the 5 training donors of each draw: 0.29 / 0.69 / 0.66 / 0.59 / 0.60, which beats the generated cells in 4 of 5 draws.\n- Adjusting for bulk ISG removes the time slope:\n  - observed −0.013 (p 0.32); wave 1 −0.019 (p 0.35);\n  - bulk-only −0.010 to +0.008 (p ≥ 0.39).\n- The generated share does add some sample-level information beyond ISG (p 0.013 / 1e-7 / 0.04 / 0.006 / 0.67), but none about the time trend.",
+     "issues": [
+      "The cell type in the claim is mislabelled. The AIFI L2 'Proliferating NK cell' label mostly marks cells that are not dividing. At equal depth, 7.1% of labelled cells express any of the 6 cycling genes, against 20.2% of 'Proliferating T' and 34.9% of plasma cells. Truly cycling NK cells, defined without labels, stay flat over 3 weeks in observed cells (0.58 / 0.58 / 0.61% of cells; slope −0.007 per day, p 0.55). So 'proliferating NK cells fall over 3 weeks' is contradicted. What falls is the share of NK cells that carry the label.",
+      "What the label actually tracks is IFN-stimulated NK cells. ISG-high NK cells are 2.6x more likely to receive the label (0.36 vs 0.14). Cells that are both labelled and ISG-high carry 85% of the observed drop (64% in wave 1) and 68-77% of the bulk-only drop in draws 0-2. The AIFI atlas itself keeps ISG+CD56low NK and Proliferating NK as separate types (Gong 2025 Fig. 1e, PDF p.2), so this looks like a label-transfer artifact.",
+      "This is already reported for this cohort. Stephenson 2025 Fig. 4A (PDF p.8) shows IFN-stimulated NK cells raised about 4 log2 over COVID− in weeks 1-2 and lower in week 3. The text on PDF p.9 says IFN-stimulated cell types, including NK, rise in the first week, 'gradually reduced over time' and persist 'into weeks 2 and 3'. Fig. 4B (PDF p.8) shows the NK IFN-alpha signature declining. The broader literature also describes NK activation with MKI67 and ISGs in COVID (Wilk 2021 p.10, Fig. 6 p.11), more cycling and activated NK cells in hospitalised COVID (COMBAT 2022 p.10), and IFN-alpha2 with IL-15 driving cycling CD38+HLA-DR+ T and NK cells (Huang 2023 p.7-8). Nothing new is shown.",
+      "'In every draw' overstates the evidence. The time contrast passes calibration only in draws 0 and 2 across all waves (draw 2 alone under the strict gate), and in draws 0, 1 and 2 for wave 1. On the same paired test samples, the generated slope is significantly steeper than observed in draws 0, 3 and 4 (difference −0.044, −0.070, −0.060; p ≤ 2e-4). Gated headline: draw 0 −0.073 and draw 2 −0.056 per day (all waves), plus draw 1 −0.046 for wave 1.",
+      "The steeper generated slope comes from the generator itself. Its response to the bulk ISG score is 1.5-2.7x the observed response (generated ISG coefficient 0.52-0.91 vs observed 0.33-0.38; p ≤ 0.016 in every draw). The draw-to-draw slope differences are a draw-specific amplification of this IFN dependence, not added biology.",
+      "'Most common at days 0-7' is a pattern of wave 2 only. In observed wave-1 cells (the bulk-only set is 88% wave 1), days 0-7 and 8-14 are equal (0.026 vs 0.027), the curve peaks around days 7-10 and falls only in week 3 (0.012). The claim's '0.063 → 0.036 → 0.025' pools both waves and is dominated by wave 2 (0.091 in days 0-7). Bulk-only cells fall steadily from week 1, but this comes from early, ISG-high samples of donors with no single cells. On paired wave-1 test samples the generator reproduced the flat weeks 1-2.",
+      "The bulk-only decline is carried by donors with no single cells. In bulk-only visits of the 26 donors who also have paired visits (50 samples), no draw shows a significant decline (−0.045 / +0.009 / +0.020 / −0.040 / −0.028; p ≥ 0.08), and draws 1 and 2 are positive. Yet these donors' bulk ISG falls (−0.063 z per day, p 2e-4) and their own paired visits fall in observed cells (−0.046 per day). After ISG adjustment the slope in this subgroup is even positive (+0.039 p 0.03 in draw 1; +0.082 p 0.014 in draw 2). The day × no-single-cell interaction is significant in draws 1, 2 and 4.",
+      "Individual donors do not decline consistently in the calibrated draws. In bulk-only samples, 51% of donors decline in draw 1 (sign-test p 1.0) and 60% in draw 2 (p 0.31), even though the mixed-model and within-donor slopes are significant.",
+      "Nothing here goes beyond the bulk. The bulk ISG score alone predicts the observed share better than the generated cells in all 5 draws (Spearman 0.54-0.63 vs 0.43-0.54). A 3-score linear bulk model trained on the same 5 donors beats the generated cells in 4 of 5 draws. ISG adjustment removes the slope in observed and generated cells. The claim's own 'not beyond the bulk IFN clock' is correct and decisive for novelty.",
+      "Agreement across draws is not independent replication. All draws share seed 20260927, so '5/5 same sign' counts the same starting noise five times. The per-draw calibration gates above are the relevant evidence.",
+      "Minor: '1.2-2.2x in wave 1' compares bulk-only wave-1 slopes with the observed paired wave-1 slope, which are different samples. On the same samples the wave-1 ratio is 0.80-2.05 and not significantly different from 1 in any draw."
+     ],
+     "reasoning": "I could not refute the measured trend, but the claim's biology is wrong and its result is not new.\n\nWhat holds up:\n- The share of NK cells labelled \"Proliferating NK\" falls with days since swab, robustly, in observed paired cells: −0.045 per day, within-donor −0.043, 71% of donors decline.\n- calibration.csv supports the feature (ratio 0.40-1.04, Spearman 0.45-0.59) and the sign of the COVID+/− and severity contrasts in 5/5 draws.\n- On COVID+ test samples, the recomputed time contrast has the right sign in 5/5 draws.\n- The candidate's numbers reproduce, including the bulk-only slopes and the slope removal after ISG adjustment.\n\nWhat does not hold:\n- Magnitude: the time contrast is calibrated in magnitude only in draws 0 and 2 (strictly only draw 2), and in draws 0-2 for wave 1. On the same samples, draws 0, 3 and 4 are significantly too steep, because the generator over-amplifies the IFN dependence.\n- Biology: the label mostly marks non-dividing, ISG-enriched NK cells. Truly cycling NK cells, defined from marker genes, do not decline in observed cells. The decline is carried by ISG-high labelled cells. So the 'proliferating NK' reading is contradicted.\n- What remains is the decline of IFN-stimulated NK cells, which Stephenson 2025 already reports for this cohort (Fig. 4A on PDF p.8; text on PDF p.9).\n- Beyond bulk: the bulk ISG score alone, or a simple linear bulk model trained on the same 5 donors, tracks the observed share as well as or better than the generated cells.\n- Shape and subgroups: the 'peak in days 0-7' does not hold in wave 1, which is where the bulk-only samples come from. The bulk-only decline is absent in bulk-only visits of donors who have paired single-cell visits.\n\nVerdict: qualified, not confirmed, because calibration covers only some draws and the biology contradicts the proliferation reading. Not fully refuted, because the label-share trajectory itself is supported by the observed data and by its direction in calibration. A faithful restatement would be:\n\n\"The share of NK cells labelled 'Proliferating NK' (mostly non-cycling, ISG-enriched) falls with the IFN clock. This holds in calibrated draws 0 and 2 (−0.073 and −0.056 per day; draw 1 too in wave 1) and in donors without single cells. It is fully explained by the bulk ISG score and matches the IFN-stimulated NK decline already reported by Stephenson 2025.\"\n\nStrength: weak, no novelty.\n\nAll work was CPU-only, with no GPU use; files were written only under the verify directory. No paper-archive update was made; the coordinating agent owns that step.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v09_key_numbers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/vutil.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/run.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v01_time_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v01_calibration_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v01_time_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v01_pooled.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v02_bulkonly.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v02_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v03_observed.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v03_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v03_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v04_bulk_beyond.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v04_beyond.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v04_isg_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v04_score_traj.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v04_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05_label_biology.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05_label_markers.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05_marker_state_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05b_label_drivers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05b_label_by_isg.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05b_cycling_controls.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05c_native_cycling_nk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v05c_native_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v06_bulk_drivers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v06_drivers_paired.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v06_drivers_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v07_label_decomposition.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v07_decline_attribution.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v08_robustness.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v08_depth_matched_controls.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v08_cycling_nk_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v10_bulk_linear_baseline.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v10_bulk_baseline.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/v11_lit_quotes.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/lit/quotes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-3_calib/lit/EXTRACT.sh",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-LONG-4",
+   "lens": "imp_longitudinal",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 15,
+    "theme": "dendritic cells",
+    "synthesis_strength": "weak: a bulk-driven cDC2 rise that coincides with the observed wave-1 recovery; unstable calibration; false rise in wave 2",
+    "reportable_statement": "Generated cDC2 share rises over days in bulk-only COVID+ samples (+0.049 to +0.087 logit/day in wave 1), robust to confounders and coincident with the observed wave-1 rise (+0.055, p 0.006). The calibration is weak and unstable, the observed rise is mostly between donors and is part of a myeloid-wide rise (cDC2 relative to monocytes falls), and the same generators produce a rise in wave 2 where observed cDC2 is flat. A 3-gene bulk cDC2 score tracks the truth better.",
+    "carrying_draws": {
+     "all-wave time gate": "3,4",
+     "wave-1 time gate": "0,1",
+     "both": "none",
+     "route (cDC2 relative to monocytes)": "3 only"
+    },
+    "honesty": {
+     "calibration": "calibration.csv ratio 0.55-1.29, Spearman 0.12-0.34 (COVID+ 0.04-0.34); draw 2 has wrong COVID and severity signs. Time slope on the same samples: overshoot in draws 1-2 over all waves (from wave 2), undershoot in draws 3-4 in wave 1 (0.27-0.43x).",
+     "replication": "5/5 positive in bulk-only (wave 1 +0.049 to +0.087); no-single-cell donors 4/5 (draw 3 p 0.068). Shared seed.",
+     "observed": "Observed 0.0165 -> 0.0226 -> 0.0223 (COVID- 0.026); wave 1 +0.055 (p 0.006) but within-donor +0.049 (p 0.13); wave 2 flat; week-1 depletion vs COVID- borderline (p 0.058).",
+     "beyond_bulk": "None. The 3-gene bulk score (CD1C, FCER1A, CLEC10A) tracks the observed share better in every draw (0.42-0.48 vs 0.03-0.35). Whether bulk removes the observed slope depends on the gene set."
+    },
+    "verdict_reasons": [
+     "Calibration weak and draw-unstable; no draw passes everything.",
+     "Observed recovery mostly between donors; route differs (myeloid-wide rise).",
+     "Generated rise is a week-3 step; observed rises by week 2.",
+     "False rise in wave 2 (draws 0-2 +0.084 to +0.120 where observed is flat): the generator follows falling ISG and glucocorticoid genes.",
+     "Calibration transfer doubtful at days 0-7 (bulk-only cDC2 markers start 0.56 SD lower and rise 4x faster).",
+     "Novelty overstated (Stephenson Fig. 4A shows DC3 depletion and recovery)."
+    ],
+    "refuted_subclaims": [
+     "rises from week 1 to weeks 2-3",
+     "overshoot beyond the observed cells (input differences)",
+     "recovery not reported"
+    ],
+    "new_vs_cohort_papers": "Partly reported: Stephenson 2025 Fig. 4A (non-IFN DC3 depleted in weeks 1-2, near baseline by week 3; PDF p.8-9); Gisby 2022 p.6 (fewer cDC2 by flow, wave 2); DC depletion in COVID widely reported (Stephenson 2021 p.2; COMBAT 2022 pp.4, 8; Wilk 2021 p.2).",
+    "next_steps": [
+     "Calibrate cDC2 relative to monocytes and within donors.",
+     "Separate the ISG and glucocorticoid dependence of generated cDC2."
+    ],
+    "key_sources": [
+     "verify/IMP-LONG-4_recompute/v09_summary.txt",
+     "verify/IMP-LONG-4_recompute/v04_temporal_calib.csv",
+     "verify/IMP-LONG-4_recompute/v06_compartments.csv",
+     "verify/IMP-LONG-4_recompute/v05_transfer.csv",
+     "verify/IMP-LONG-4_calib/v01_time_calibration.csv",
+     "verify/IMP-LONG-4_calib/v05_windows.csv",
+     "verify/IMP-LONG-4_calib/v05_gc.csv",
+     "verify/IMP-LONG-4_calib/v04_tracking.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-LONG-4",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only donors, the generated cDC2 share rises from week 1 to weeks 2-3 (+0.049 to +0.087 logit/day in every draw, also in donors without single cells). This matches the observed wave-1 recovery (+0.055/day). However, the bulk cDC2 marker genes already carry this rise, and the generated cells overstate it beyond what the observed cells show.",
+    "kind": "trajectory",
+    "feature": "l2 | cDC2",
+    "comparison": "Days since first positive swab (0-21 d). COVID+ samples. Bulk-only generated cells vs observed paired cells (all waves and wave 1).",
+    "effect_bulkonly": "Raw window means, d0-7/d8-14/d15-21/>21:\n- d0: 0.016/0.017/0.025/0.026\n- d1: 0.020/0.020/0.035/0.037\n- d2: 0.033/0.027/0.059/0.055\n- d3: 0.012/0.011/0.016/0.016\n- d4: 0.014/0.017/0.020/0.018\n\nSlopes (logit/day):\n- Severity-adjusted: +0.053, +0.069, +0.056, +0.054, +0.049.\n- Wave 1: +0.063, +0.087, +0.056, +0.051, +0.049.\n- No-single-cell donors: +0.054, +0.087, +0.058, +0.034, +0.050.",
+    "stats": "Model: MixedLM as above.\n- All-wave p <= 0.002 (BH q <= 0.01).\n- Wave-1 q: 1e-5 to 0.028.\n- Within-donor FE slope: +0.050 to +0.104 (p <= 0.003).",
+    "draws_same_sign": "5/5. Shared seed, so not independent.",
+    "paired_calibration": "calibration.csv: ratio 0.55-1.29, Spearman 0.12-0.34; COVID+/- sign 4/5, severity sign 4/5.\nCOVID+ test samples: Spearman 0.04-0.34; TIME_PASS in draws 3 and 4 only.\nWave 1: ratio 0.91-2.45, Spearman 0.21-0.59, slope ratio 0.27-1.09; W1_TIME_PASS in draws 0 and 1.\nThe all-wave and wave-1 gates pass in different draws, so the calibration is unstable.",
+    "paired_observed_concordance": "Partial.\n- Observed: 0.0165 -> 0.0226 -> 0.0223 (COVID- 0.026). Severity-adjusted slope +0.034 (p 0.01).\n- But the within-donor FE slope is only +0.025 (p 0.2), and per-donor Wilcoxon p is 0.52.\n- By wave: +0.055 in wave 1 (p 0.006), +0.015 in wave 2.\n- Bulk-only FE slopes are 2-4x the observed FE slope.",
+    "beyond_bulk": "Nothing beyond the bulk, and an overshoot.\n- The bulk cDC2 score (FCER1A, CLEC10A, MRC1, CD1C, PKIB, ...) rises in both sets (+0.063 / +0.056 SD/day).\n- Adjusting for it removes the observed slope (-0.0007, p 0.95; wave 1 -0.005, p 0.76).\n- The generated cells keep +0.034 to +0.047 (p <= 0.016) after the same adjustment, a residual the observed cells do not have.\n- The bulk score tracks the observed share better (Spearman 0.36 vs 0.18; within-donor 0.62 vs 0.27).",
+    "novelty": "Gisby 2022 PDF p.6: flow cytometry showed fewer cDC2 in COVID+ than before infection (wave 2). Neither cohort paper reports the recovery over time.",
+    "strength": "weak",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s03_temporal_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_adjusted.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_bulk_traj.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s09_shapes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s13_within_donor.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s17_wave1_beyond_bulk.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s21_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "ac579b697713bbba6",
+     "verdict": "qualified",
+     "recomputed": "I recomputed everything with my own code, starting from the raw cells.parquet files, bulk npz matrices and meta. My per-sample cDC2 counts match composition.parquet exactly (1692 of 1692 rows). Models use logit fraction ~ days + peak severity (+ wave 2) with a donor random intercept, COVID+ samples, days <= 21.\n\n**Bulk-only generated cells, 127 samples from 57 donors**\n- Window means (d0-7 / d8-14 / d15-21 / >21) match the claim exactly:\n  - d0: .016/.017/.025/.026\n  - d1: .020/.020/.035/.037\n  - d2: .032/.027/.059/.055\n  - d3: .012/.011/.016/.016\n  - d4: .014/.017/.020/.018\n- Severity-adjusted slope: +0.053, +0.069, +0.056, +0.054, +0.049 (p 2.5e-4, 1.3e-4, 0.002, 9.3e-4, 2.1e-7).\n- Wave 1 only: +0.063, +0.087, +0.056, +0.051, +0.049.\n- Donors without single cells: +0.054, +0.087, +0.058, +0.034, +0.050. Draw 3 is not significant (p 0.068).\n- Within-donor fixed-effect slope: +0.085, +0.104, +0.081, +0.087, +0.050. Within-donor permutation p <= 4e-4. Per-donor Wilcoxon p <= 0.007.\n\n**Observed paired cells**\n- Windows 0.0165 / 0.0226 / 0.0223; COVID- 0.0262.\n- Severity-adjusted slope +0.034 (p 0.010). Wave 1 +0.055 (p 0.006). Wave 2 +0.015 (p 0.39).\n- Within-donor slope +0.025 (p 0.20; permutation p 0.11). Wave-1 within-donor slope +0.049 (permutation p 0.059). Per-donor Wilcoxon p 0.52.\n\n**Confounder battery (bulk-only)**\nThe slope stays positive with p < 0.05 in 5 of 5 draws under every one of these controls:\n- age + sex; ethnicity\n- WHO severity at the sample; fatal outcome; diabetes\n- bulk library size; per-cell library size; all covariates together\n- days since symptom onset instead of days since swab\n- dropping the sex_flag sample and samples from draw-training donors (+0.051 to +0.076)\n- label confidence >= 0.9 (+0.053 to +0.072)\n- cDC2 counted within DC, or over non-platelet/erythrocyte cells\n- within-donor variants\n\ncDC2 counted within the myeloid compartment holds in 4 of 5 draws. Leaving out one donor at a time gives max p 0.0065 for bulk-only and 0.034 for observed.\n\n**Calibration**\n- calibration.csv rows recompute identically:\n  - ratio 0.91, 1.09, 1.29, 0.55, 0.77\n  - Spearman 0.26, 0.19, 0.12, 0.34, 0.32\n  - COVID+/- and severity signs correct in draws 0, 1, 3, 4 and wrong in draw 2\n- Temporal check on COVID+ paired test samples (<= 21 d):\n  - generated slope +0.062, +0.073, +0.070, +0.023, +0.030 vs observed +0.039, +0.033, +0.032, +0.038, +0.032\n  - Spearman 0.18, 0.16, 0.04, 0.34, 0.34\n  - the gate passes in draws 3 and 4\n- Wave 1: the gate passes in draws 0 and 1 (slope ratios 0.79, 1.09, 0.51, 0.43, 0.27).\n- Generated minus observed slope on the same samples:\n  - all waves: +0.020 (p 0.28), +0.042 (p 0.021), +0.040 (p 0.057), -0.016, -0.003\n  - wave 1: -0.014, +0.002, -0.027, -0.037, -0.041 (p 0.027)\n  - wave 2: +0.064, +0.088, +0.121 (p <= 0.016), +0.008, +0.030\n\n**Bulk**\n- Canonical cDC2 score (CD1C, CLEC10A, FCER1A): bulk-only +0.076 z/day (p 1e-10; wave 1 +0.089) vs paired +0.018 (p 0.15; wave 1 +0.044).\n- Bulk-only d0-7 samples sit 0.56 SD lower on this score than paired d0-7 samples (p 0.011).\n- The agent's 10-gene score: +0.059 (bulk-only) vs +0.062 (paired).\n- Observed slope after adjusting for the canonical score: +0.028 (p 0.026). After the 10-gene score: +0.005 (p 0.69).\n- Bulk-only slope after the canonical score: +0.029 to +0.054 (p <= 0.02).\n\n**Compartments**\n- Observed: monocyte share +0.058/day (wave 1 +0.106), DC share +0.003, cDC2:monocyte log ratio -0.021 (wave 1 -0.045, p 0.027).\n- Bulk-only cDC2:monocyte: +0.043, +0.044, +0.038, +0.013 (not significant), +0.053.\n- Paired test generated, wave 1: +0.036, +0.025, +0.044, +0.003, +0.026.\n\n**Window shape**\n- Bulk-only d8-14 vs d0-7 is +0.06 to +0.12 logit (not significant) in draws 0-3. The rise comes at d15-21 (+0.60 to +0.83).\n- Observed d8-14 vs d0-7 is +0.31 (p 0.049); d15-21 vs d8-14 is +0.13 (p 0.49).",
+     "issues": [
+      "Weak, unstable calibration: draws 0 and 1 have Spearman below 0.3. Draw 2 fails calibration.csv: wrong COVID+/- and severity signs, Spearman 0.12, and 0.04 among COVID+ test samples, so it cannot carry the result. The temporal gate passes in draws 3 and 4 over all waves, but in draws 0 and 1 for wave 1; no draw passes both. Gated headline: draws 3 and 4 give +0.054 and +0.049 logit/day (all waves); draws 0 and 1 give +0.063 and +0.087 (wave 1). Both generation seeds (20260927 and 840262485, confirmed in the RECEIPTs) are shared across draws, so 5 of 5 is not independent replication.",
+      "The observed 'recovery' is mostly between donors. Within donors it is +0.025/day (p 0.20; permutation p 0.11), +0.049 in wave 1 (permutation p 0.059), and the per-donor Wilcoxon p is 0.52.",
+      "The match to the observed rise holds only for the share of all cells; the route differs. The observed rise follows a myeloid-wide rise: monocyte share +0.058/day, +0.106 in wave 1. Relative to monocytes, observed cDC2 falls (wave 1 -0.045/day, p 0.027). Generated cDC2 rises relative to monocytes in 4 of 5 bulk-only draws (+0.038 to +0.053). On the same paired test samples in wave 1 it also rises in 4 of 5 draws (+0.025 to +0.044, p <= 0.024). Only draw 3 follows the observed route.",
+      "The shape claim 'rises from week 1 to weeks 2-3' is inaccurate for bulk-only donors. Adjusted for severity and wave, d8-14 vs d0-7 is only +0.06 to +0.12 logit (not significant) in draws 0-3, and the rise appears at d15-21. Observed cells rise by d8-14 (+0.31, p 0.049) and then plateau (d15-21 vs d8-14 +0.13, p 0.49).",
+      "Comparing bulk-only slopes with the observed slope mixes in different bulk inputs, so it does not show an overshoot. The bulk-only donors' canonical cDC2 bulk markers rise about 4x faster than the paired donors' (+0.076 vs +0.018 z/day; within-donor +0.072 vs +0.023). Their d0-7 bulk starts lower (core score -0.56 SD, p 0.011; FCER1A -0.61 SD, p 0.003), so calibration does not transfer at d0-7. 'Bulk-only within-donor slopes are 2-4x the observed one' therefore compares different inputs.",
+      "On the same paired samples, the generator overshoots only in draws 0-2 over all waves (+0.020 p 0.28, +0.042 p 0.021, +0.040 p 0.057). This comes from wave 2, where generated cells rise +0.084 to +0.120/day while observed cells are flat (overshoot p <= 0.016). In wave 1 there is no overshoot, and draw 4 undershoots (-0.041, p 0.027). The bulk-only set is 88% wave 1.",
+      "'Adjusting for the bulk score removes the observed slope' depends on the gene set. It holds for the agent's 10-gene score (FCER1A, CLEC10A, MRC1, CD1C, PKIB, FLT3, IL1R2, RTN1, DEPTOR, P2RY6): +0.005, p 0.69. It fails for canonical CD1C/CLEC10A/FCER1A: +0.028, p 0.026. That canonical score does not rise in paired donors (+0.018, p 0.15; FCER1A alone -0.034, p 0.019). Generated cells add to the canonical score in predicting the observed share in draws 1, 2 and 4 (p <= 0.041), but not to the 10-gene score, which was chosen from this cohort's observed pseudobulk.",
+      "The novelty claim is overstated. Stephenson 2025 Fig. 4A, from this same cohort, shows non-IFN-stimulated DC3 (a cDC2-family subset) clearly depleted in weeks 1-2 vs COVID- and near baseline by week 3, so a temporal DC recovery is partly reported already. The Gisby 2022 p.6 flow statement (fewer type 2 DCs in COVID+ vs pre-infection, wave 2) is confirmed.",
+      "Minor points. In donors without single cells, draw 3 is not significant (+0.034, p 0.068). calibration.csv's 'severe/critical vs mild/moderate (COVID+)' contrast actually includes 8-10 recovery samples per draw (n 49 vs 62 etc.); the cDC2 signs do not change. The bulk ISG (interferon clock) score explains most of the trajectory: observed slope +0.019 (p 0.29) after adjustment, bulk-only wave-1 slopes p 0.018 to 0.11."
+     ],
+     "reasoning": "Not refuted: the generated pattern reproduces exactly and no confounder control removes it. It is qualified because the calibration is weak and unstable, and several of the claim's parts do not hold as stated.\n\n**What holds**\n- My own code, run on the raw cells.parquet files, reproduces every quoted bulk-only number: window means, the slopes (severity-adjusted, wave 1, no-single-cell donors, within-donor), the observed truth, and the calibration ranges.\n- The bulk-only rise survives every confounder I tried, in 5 of 5 draws:\n  - age, sex, ethnicity, current WHO severity, fatal outcome, diabetes;\n  - bulk and per-cell library size; symptom-onset day definition; wave 1 only;\n  - dropping the sex_flag sample and samples from draw-training donors;\n  - label confidence >= 0.9 and other denominators;\n  - donor-aware fixed effects, within-donor permutation, per-donor slopes and leave-one-donor-out.\n- The generated 'cDC2' cells carry no basophil genes, so the trend is not a basophil labelling artefact.\n\n**Why qualified, not confirmed**\n1. Calibration is weak and unstable. calibration.csv has Spearman 0.12-0.34 and draw 2 has wrong contrast signs. The temporal gate passes in draws 3 and 4 over all waves but in draws 0 and 1 for wave 1, so the carrier draws depend on the gate and draw 2 never qualifies.\n2. The observed 'recovery' it is said to match is mostly between donors (within-donor p 0.11-0.20). It is also not cDC2-specific: the observed cDC2 share rises with monocytes, while cDC2 relative to monocytes falls in wave 1. On the same paired samples, the generator raises cDC2 relative to monocytes in 4 of 5 draws, so it matches the share of all cells by the wrong route. Only draw 3 gets the route right.\n3. The bulk-only rise sits in week 3, not weeks 2-3, unlike the observed week-2 rise.\n4. The 'overshoot' argument compares different inputs. The bulk-only donors' own bulk cDC2 markers rise about 4x faster from a lower d0-7 start, so calibration does not transfer at d0-7. On matched paired samples, the overshoot appears only in draws 0-2 and is driven by wave 2; there is none in wave 1, which makes up 88% of the bulk-only set.\n5. 'The bulk removes the observed slope' holds for the agent's 10-gene score but not for canonical CD1C/CLEC10A/FCER1A.\n6. Novelty is partly pre-empted by Stephenson 2025 Fig. 4A (DC3 depleted in weeks 1-2, near baseline by week 3).\n\n**Overall**\nThe finding is a real, robust generated-cell pattern that the bulk already contains. Its agreement with the observed cells is superficial, and it should stay 'weak'.\n\n**Scope**\nCPU only; no GPU work. All writes are in my verify dir. This verifier did not publish anything to the paper archive; the coordinating agent owns any task-end archive update.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v09_summary.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v09_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v00_lib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/run.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v01_build.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v01_samples.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v01_check.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v01_bulk_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v02_effect.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v02_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v02_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v03_confounders.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v03_confounders.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v03_design.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v04_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v04_calib_check.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v04_temporal_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v04_bo_vs_pt.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v05_beyond_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v05_bulk_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v05_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v05_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v05_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_robustness.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_compartments.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_permutation.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_jackknife.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_adds.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v06_windows_adj.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v07_csv_severity_rows.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v07_csv_severity_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v08_cdc2_identity.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v08_cdc2_identity_levels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v08_cdc2_identity_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v10_paper_check.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v10_stephenson2025_fig4A_crop.png",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v10_stephenson_grep.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_recompute/v10_gisby_grep.txt"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "adedbb7284354e010",
+     "verdict": "qualified",
+     "recomputed": "All numbers come from my own scripts in the verify dir; v07_quoted_numbers.txt collects them. Slopes are logit per day, COVID+ samples up to day 21. Model: mixed model with donor random intercept, adjusted for peak severity (+ wave).\n\n1. CALIBRATION (calibration.csv, recomputed on each draw's paired TEST samples)\n- Ratio 0.91, 1.09, 1.29, 0.55, 0.77.\n- Spearman 0.26, 0.19, 0.12, 0.34, 0.32; on COVID+ test samples only 0.18, 0.16, 0.04, 0.34, 0.34.\n- COVID+/- sign and severity sign are both right in draws 0, 1, 3, 4; draw 2 is wrong on both.\n- Contrast gate (both signs right, Spearman >= 0.2, ratio 0.5-2) passes draws 0, 3, 4.\n\n2. TIME CONTRAST (generated vs observed on the same test samples)\n- All waves: generated +0.062, +0.073, +0.070, +0.023, +0.030 vs observed +0.039, +0.033, +0.032, +0.038, +0.032. Generated minus observed slope: +0.020 (p 0.28), +0.042 (p 0.021), +0.040 (p 0.057), -0.016, -0.003. Time gate passes draws 3, 4.\n- Wave 1: generated +0.044, +0.062, +0.028, +0.027, +0.015 vs observed +0.055, +0.057, +0.055, +0.063, +0.057. Slope ratio 0.79, 1.09, 0.51, 0.43, 0.27. Draw 4 difference -0.041 (p 0.027). Time gate passes draws 0, 1 (draw 2 borderline).\n- Wave 2: observed is flat (+0.023, +0.005, +0.004, +0.019, +0.007; all not significant), but generated is +0.084, +0.088, +0.120, +0.024, +0.044. Difference p 0.016, 0.0007, 0.0001 in draws 0-2.\n- Per-donor change from week 1 to later: generated and observed signs agree in 0.46-0.67 of donors; Spearman across donors 0.05-0.25.\n- Draws that carry the result: all-wave gate draws 3, 4; wave-1 gate draw 0; no draw passes all gates.\n\n3. OBSERVED TRUTH (113 samples / 35 donors)\n- All waves: window means 0.0165 / 0.0226 / 0.0223 (COVID- 0.0262).\n  - Severity-adjusted slope +0.034 (p 0.010); population-averaged binomial model +0.028 (p 0.019).\n  - Within-donor slope +0.025 (p 0.20); per-donor slopes 61% positive, Wilcoxon p 0.52.\n- Wave 1: 0.0100 / 0.0129 / 0.0152.\n  - Slope +0.055 (p 0.006); population-averaged model +0.038 (p 0.001); within-donor rank correlation 0.34 (p 0.019).\n  - Within-donor slope +0.049 (p 0.13, 16 donors); per-donor Wilcoxon p 0.16.\n- Wave 2: +0.015 (p 0.39); within-donor slope +0.004.\n- Week 1 vs COVID-: Mann-Whitney p 0.058.\n\n4. BULK-ONLY GENERATED (127 samples / 57 donors)\n- Slope: all waves +0.053, +0.069, +0.056, +0.054, +0.049; wave 1 +0.063, +0.087, +0.056, +0.051, +0.049; donors without single cells +0.054, +0.087, +0.058, +0.034 (p 0.068), +0.050.\n- After dropping sex-flag samples and training-donor samples: +0.059 to +0.076.\n- Within-donor slopes: all waves +0.050 to +0.104; wave 1 +0.052 to +0.134.\n- Shape (logit change vs week 1):\n  - Bulk-only, wave 1: week 2 +0.17, +0.31, +0.10, +0.01, +0.34 (only draw 4 p < 0.05); week 3 +0.65 to +1.13.\n  - Observed, wave 1: week 2 +0.42 (p 0.08); week 3 +0.79 (p 0.015).\n  - Paired-test generated, wave 1: week 2 between -0.13 and +0.12.\n\n5. BEYOND BULK\n- Bulk-score slopes, wave 1 (z per day):\n  - 3-gene marker score (CD1C, FCER1A, CLEC10A): paired +0.044, bulk-only +0.089.\n  - My data-driven cDC2 gene panel: +0.077, +0.070.\n  - The claimant's panel: +0.075, +0.081.\n- Do the bulk-only inputs match the paired ones? The 3-gene score is 0.58 SD lower in bulk-only week-1 samples (p 0.069), so the bulk-only inputs rise more steeply.\n- Tracking the observed share (all COVID+):\n  - Generated cells: Spearman 0.03-0.35; within-donor 0.19-0.32.\n  - 3-gene score: Spearman 0.42-0.48; within-donor 0.46-0.55.\n  - Generated given the 3-gene score (partial Spearman): -0.02 to +0.20.\n- Observed slope after adjusting for the bulk score:\n  - Claimant's panel: +0.005 (p 0.69); wave 1 -0.005.\n  - Data-driven panel: +0.008 (p 0.59).\n  - 3-gene panel: +0.028 (p 0.026); wave 1 +0.029 (p 0.13).\n- Bulk-only slope after the same adjustment: +0.029 to +0.054 (3-gene), +0.033 to +0.044 (data-driven), +0.036 to +0.047 (claimant's).\n- Paired-test generated, wave 1, after adjustment: -0.013 to +0.025 (3-gene), -0.022 to +0.005 (data-driven).\n- Wave 2: the observed cDC2 share falls with the glucocorticoid-response score (coefficient -0.14 to -0.28), but draws 1-2 map that score to more cDC2 (+0.42, p <= 0.01).",
+     "issues": [
+      "Weak trust layer. In calibration.csv, cDC2 Spearman is 0.12-0.34, and on COVID+ test samples only 0.04-0.34; draws 0-2 barely rank the COVID+ samples (0.18, 0.16, 0.04). Draw 2 has the wrong COVID+/- and severity direction. Only draws 0, 3 and 4 pass a contrast gate (both signs right, Spearman >= 0.2, ratio 0.5-2).",
+      "Unstable time calibration. All waves: the time gate passes only draws 3-4; draws 1-2 overshoot (generated minus observed slope +0.042, p 0.021; +0.040, p 0.057). Wave 1, which is 89% of the bulk-only set: the gate passes draws 0-1 (draw 2 borderline); draws 3-4 under-reproduce the slope (ratio 0.43 and 0.27; draw 4 difference -0.041, p 0.027). No draw passes the contrast gate and both time gates. The carrying draws are 3-4 under the all-wave gate, or draw 0 under the wave-1 gate.",
+      "The generated rise is not specific to real recovery (new; not in the claim). In wave 2 the observed cDC2 share is flat (+0.004 to +0.023 logit/day, all not significant). Draws 0-2 still generate +0.084, +0.088 and +0.120 (difference p 0.016, 0.0007, 0.0001). The generator links falling ISG to more cDC2 more strongly than the observed cells do (coefficient -0.21 to -0.44 vs -0.13 to -0.17, not significant). Draws 1-2 also map glucocorticoid-response genes to more cDC2 (+0.42), the opposite of the observed sign (-0.14 to -0.28).",
+      "Within-donor tracking is near chance. For the change from week 1 to later, generated and observed signs agree in 0.46-0.67 of donors. Spearman of the per-donor changes is 0.05-0.25 over all waves.",
+      "Observed support is partial. Wave 1: slope +0.055 (p 0.006), population-averaged binomial model p 0.001, within-donor rank correlation 0.34 (p 0.019). But the within-donor slope is +0.049 (p 0.13, 16 donors) and the per-donor Wilcoxon p is 0.16. All waves: within-donor slope +0.025 (p 0.20), Wilcoxon p 0.52. Wave 2 is flat. The week-1 depletion vs COVID- is only borderline (0.0165 vs 0.0262, p 0.058).",
+      "The shape does not match 'rises from week 1 to weeks 2-3'. In bulk-only wave 1, the week-2 change vs week 1 is +0.01 to +0.34 logit (significant only in draw 4) and the week-3 change is +0.65 to +1.13. Paired-test generated cells in wave 1 show the same pattern: flat at week 2 (-0.13 to +0.12), jump at week 3. Observed wave 1 rises by +0.42 (p 0.08) at week 2 and +0.79 (p 0.015) at week 3. The linear slopes agree; the timing does not.",
+      "The novelty claim is overstated. 'Neither cohort paper reports the recovery over time' is inaccurate for the DC3 part of cDC2. Stephenson 2025 Fig. 4A (PDF p.8 = journal p.7) shows non-IFN-stimulated DC3 strongly depleted in weeks 1-2 and close to baseline by week 3 and at recovery, with IFN-stimulated DC3 raised in weeks 1-2. The text (PDF p.9 = journal p.8) says: 'Almost all non-IFN-stimulated states returned to pre-infection levels in convalescent samples.' The total cDC2 trajectory is not stated. Fig. 2 (PDF pp.5-6) does not list DCs among the types that changed significantly in week 1 or 2. Gisby 2022 PDF p.6 (lower cDC2 by flow cytometry in wave 2) is quoted correctly. DC depletion in COVID-19 is well known: Stephenson 2021 PDF p.2 (DC2 and DC3 reduced), COMBAT 2022 PDF pp.4 and 8 (CD1c+ cDCs reduced), Wilk 2021 PDF p.2. So the biology is expected, not new.",
+      "Nothing beyond the bulk. The 3-gene bulk score (CD1C, FCER1A, CLEC10A) tracks the observed cDC2 share better than the generated cells in every draw: Spearman 0.42-0.48 vs 0.03-0.35, within-donor 0.46-0.55 vs 0.19-0.32. Given that score, the generated share adds only -0.02 to +0.20 (partial Spearman), while the score keeps 0.32-0.48 given the generated share.",
+      "Whether the bulk score 'removes the observed slope' depends on the gene panel. Data-driven panels remove it: claimant's +0.005 (p 0.69), mine +0.008 (p 0.59). The 3-gene panel does not: +0.028 (p 0.026); wave 1 +0.029 (p 0.13). With that panel, the bulk-only leftover (+0.029 to +0.054) is 1.0-1.9x the observed leftover, so the claimed overshoot is not robust to the choice of bulk score. Either way, the leftover is uncalibrated. On paired-test wave-1 samples the same generators leave almost nothing (-0.013 to +0.025), so the bulk-only leftover comes from differences in the bulk-only inputs. Their 3-gene score is 0.58 SD lower in week 1 (p 0.069) and rises at +0.089 vs +0.044 SD/day in paired.",
+      "Draw agreement is weak evidence. All five draws used seed 20260927 (shared noise), so '5/5 same sign' is not five independent replications. The training donors explain the level differences between draws (draw 3's donors are cDC2-poor at 0.013, draw 2's cDC2-rich at 0.036), but not the overshoot in wave 2.",
+      "Not checked here: later DC recovery studies (Winheim 2021 PLoS Pathog; Perez-Gomez 2021 Cell Mol Immunol) are cited from memory; they are not in the local reference folder.",
+      "No paper-archive change. This verifier wrote analysis files only under its own verify dir; the archive update under POLICY.md is left to the coordinator."
+     ],
+     "reasoning": "I tried to refute the claim on four fronts: calibration, observed support, biology and novelty, and value beyond the bulk.\n\n1. Calibration. The descriptive part holds: bulk-only generated cDC2 rises in every draw (+0.049 to +0.087 logit/day), also in donors without single cells and after the sensitivity drops. But the trust layer for cDC2 is weak (COVID+ Spearman 0.04-0.34; draw 2 has the wrong contrast direction). The time contrast passes in different draws depending on the population: draws 3-4 over all waves, draws 0-1 in wave 1. No draw passes everything.\n\nThe strongest new evidence comes from wave 2. There the observed cDC2 share is flat, yet draws 0-2 generate a significant rise. So the generator's rise follows bulk features that change with time, such as falling ISG, whether or not cDC2 actually recovers. That calls into question reading the generated rise as recovery. It does not reverse the direction where it matters: in wave 1 every draw generates a positive slope, and the gated draws (3-4 all-wave, 0 wave 1) all show a significant bulk-only rise.\n\n2. Observed support. Wave 1 shows a real rise (+0.055, p 0.006; binomial model p 0.001), but the within-donor evidence is not significant (p 0.13), wave 2 is flat, and the week-1 depletion vs COVID- is borderline. The generated rise is a week-3 step, while the observed wave-1 rise is more gradual.\n\n3. Biology and novelty. DC depletion in acute COVID-19 is well documented. The cohort's own single-cell paper (Stephenson 2025, Fig. 4A, PDF p.8) shows non-IFN DC3 depleted in weeks 1-2 and back near baseline later. So 'neither cohort paper reports the recovery' is inaccurate for the DC3 component, and the finding is not new.\n\n4. Beyond the bulk. Nothing beyond the bulk, as the claim already concedes. A 3-gene bulk score tracks the observed share better than the generated cells in every draw. The claimed overshoot leftover depends on the gene panel, and on paired-test samples the same generators leave none.\n\nOverall: the calibration and observed data qualify the claim but do not contradict its direction, and the claim already labels itself weak and bulk-derivable. So the verdict is qualified, not refuted. What survives is narrower: in wave-1 bulk-only donors the generator reproduces a bulk-driven rise in cDC2 share, gated in draw 0 (wave-1 gate) or draws 3-4 (all-wave gate). It coincides with the observed wave-1 recovery, adds nothing beyond a 3-gene marker score, and the same generators produce a false rise in wave 2.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v07_quoted_numbers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v07_literature_quotes.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v01_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v01_time_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v01_recomputed_contrasts.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v01_calibration_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v01_donor_changes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v02_observed.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v02_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v03_bulkonly_gated.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v03_bulkonly.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v03_gated_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_bulk_baseline.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_tracking.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_score_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v04_genes.json",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v05_shape_and_wave2.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v05_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v05_gc.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v06_training_donors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/img_steph_fig4A.png",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/v00_pdf_extract.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/vutil.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-4_calib/run.sh",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-PROG-6",
+   "lens": "imp_programs",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 16,
+    "theme": "monocyte CD163 / glucocorticoid",
+    "synthesis_strength": "artefact-suspect: bulk-carried critical-illness monocyte programme; calibration rests on dexamethasone-era wave-2 truth",
+    "reportable_statement": "Fatal bulk-only patients' generated CD14 monocytes carry more of a glucocorticoid/dex-monocyte programme (+0.21, +0.36, +0.40, +0.65, +0.52 log2; draws 1-4 q <= 0.005), and the bulk carries the same association (+1.49). The paired fatal truth is a wave-2, dexamethasone-era severe-illness truth; within severe/critical patients the generator fails calibration; the wave-1 signal is severity at sampling, not fatality; the core glucocorticoid targets do not rise in wave-1 fatal bulk. Treatment is not recorded.",
+    "carrying_draws": {
+     "CD14 fatal": "1-4 (draw 0 fragile)",
+     "within severe/critical": "none (calibration fails)",
+     "wave 1 after WHO adjustment": "none"
+    },
+    "honesty": {
+     "calibration": "Not in calibration.csv; CD14 fatal gate passes 5/5 but compressed (0.26-0.47x; 0.36-0.63x with log2 CPM+1); per-sample Spearman 0.53-0.60 vs bulk 0.81-0.84. 6 of 7 fatal test donors per draw are wave 2, where fatal equals severe/critical. Within severe/critical the paired check is computable and fails (-0.02 to 0.17x; draw 4 wrong sign).",
+     "replication": "5/5 positive; survives age/sex/wave, sex flag, training donors, repeated measures, label confidence. Removed by WHO at sampling within wave 1 (+0.01 to +0.21, p >= 0.12) and by the full covariate set. Draw effect sizes track the fatal training donor's observed dex score (Spearman 0.87-0.97, n = 5).",
+     "observed": "Observed CD14 fatal +0.94 (p < 0.001), severity +0.70; within severe/critical +0.74 (p 0.07); after severity at sample + wave +0.53 (p 0.10). Fatal labels disagree across sources (Zenodo 7 wave-2 deaths; Gisby 4; single-cell metadata C101, C146, C147 'no').",
+     "beyond_bulk": "None: likelihood-ratio p 0.06-0.95 for adding generated CD14 to the bulk score."
+    },
+    "verdict_reasons": [
+     "Paired fatal truth = severe illness in the dexamethasone era.",
+     "Within-severe calibration fails.",
+     "Wave-1 signal is severity at sampling; core GR targets (TSC22D3, ZBTB16, PER1, KLF9, DDIT4) rise only in wave 2.",
+     "Generator misses the TSC22D3 rise; GR core also rises in T, NK, B (body-wide).",
+     "Draw effect sizes imprint the fatal training donor.",
+     "Nothing beyond bulk."
+    ],
+    "refuted_subclaims": [
+     "TSC22D3 and VSIG4 rise in generated monocytes",
+     "a glucocorticoid-response signal in wave-1 fatal patients",
+     "within-severe result uncalibratable (it is calibratable and fails)"
+    ],
+    "new_vs_cohort_papers": "Dex-monos in wave 2 only: Stephenson 2025 PDF p.11; 8/17 wave-2 patients received glucocorticoids: Gisby 2022 PDF p.2. Biology precedent: COMBAT 2022 p.8 (FKBP5/CD163 module), Bernardes 2020 p.15 (fatality transcripts incl. TSC22D3, PER1, ADAMTS2, TPST1 in monocytes).",
+    "next_steps": [
+     "Obtain steroid exposure and reconcile the fatal labels.",
+     "Report as a critical-illness monocyte programme, adjusted for WHO at sampling."
+    ],
+    "key_sources": [
+     "verify/IMP-PROG-6_recompute/v05_summary.csv",
+     "verify/IMP-PROG-6_recompute/v04_2_sev_adjust_bulk_vs_gen.csv",
+     "verify/IMP-PROG-6_recompute/v03h_imprinting.csv",
+     "verify/IMP-PROG-6_recompute/v03d_specificity.csv",
+     "verify/IMP-PROG-6_calib/v05_key_numbers.csv",
+     "verify/IMP-PROG-6_calib/v01_calib.csv",
+     "verify/IMP-PROG-6_calib/v02_bulk_gc_genes.csv",
+     "verify/IMP-PROG-6_calib/v04_wave1_critical_contrast.csv",
+     "verify/IMP-PROG-6_calib/v00_fatal_label_crosscheck.txt"
+    ]
+   },
+   "finding": {
+    "id": "IMP-PROG-6",
+    "cohort": "imperial_eskd",
+    "claim": "Fatal outcome in bulk-only patients comes with a glucocorticoid-response programme that the generated cells place inside monocytes. Stephenson's dex-monocyte genes plus FKBP5, TSC22D3, VSIG4 and ZBTB16 rise in CD14, CD16 and intermediate monocytes. The rise is also present in wave-1 (pre-dexamethasone) patients and within severe/critical patients. But the paired truth is almost entirely dexamethasone-era wave-2 samples and treatment is not recorded, so the result is confounded by treatment, and it adds nothing to the bulk.",
+    "kind": "program",
+    "feature": "Dex-mono / glucocorticoid programme: CD163, ADAMTS2, SLC1A3, SESN1, FPR1, MTSS1, MFGE8, MRC1, FKBP5, TSC22D3, VSIG4, ZBTB16 (the genes expressed in the type).",
+    "comparison": "Fatal vs survived among COVID+ (12 vs 46 donors); also within severe/critical patients (12 vs 16) and wave-1 samples only.",
+    "effect_bulkonly": "CD14 fatal, draws 0-4: +0.21, +0.36, +0.40, +0.65, +0.52 (draw 3 group means 6.47 vs 5.82). Intermediate monocytes +0.24 to +0.47; CD16 +0.22 to +0.58. Wave-1 only, CD14: +0.13, +0.30, +0.24, +0.47, +0.45 (p 0.31, 0.016, 0.084, 0.030, 0.023). Within severe/critical patients: +0.23 to +0.56 (p <= 0.03).",
+    "stats": "Donor-aware Welch test. Drawwise BH q for CD14 fatal: 0.058, 0.001, 0.005, 0.003, 0.001; carried by draws 1-4. The within-severe and wave-1 analyses are exploratory, because no calibration is possible for them.",
+    "draws_same_sign": "5/5 draws positive (fatal, severity, within-severe and wave-1).",
+    "paired_calibration": "The CD14 fatal gate passes in 5/5 draws, but the generated contrast is only 0.26-0.47x of observed. Per-sample Spearman 0.53-0.60, against 0.81-0.84 for the bulk score (partial -0.03 to 0.15). The paired fatal truth comes from 7 test donors, and 28 of their 31 samples are wave 2 (dexamethasone era). Only one fatal paired donor is from wave 1, so no wave-1 calibration is possible. This is not a calibration.csv feature.",
+    "paired_observed_concordance": "Observed CD14: fatal +0.94 (8 vs 26 donors, p < 0.001); severity +0.70 (p 0.004); wave-1 severity +0.46 (p 0.17); within severe/critical +0.74 (8 vs 5 donors, p 0.07). Stephenson's dex-monocytes occur only in wave 2.",
+    "beyond_bulk": "None. The top fatal-up bulk genes are already glucocorticoid-response genes: ADAMTS2 +3.17, IL1R2 +3.02, TPST1 +1.98, SLC1A3 +1.94, CD163 +1.88 and FKBP5 +1.52 log2. The 11-gene bulk score is +1.49 (p < 0.001; wave-1 +1.06, p 0.023; paired bulk +0.79). The generated CD14 score adds nothing to the bulk score (likelihood-ratio p 0.06-0.95). The monocyte location matches what a reference atlas would say.",
+    "novelty": "Dex-monocytes after glucocorticoids in wave 2 are reported in Stephenson 2025, PDF p.11. Gisby 2022, PDF p.2, reports that 8 of 17 wave-2 patients received glucocorticoids. A glucocorticoid-response signal in wave-1 fatal patients is not reported. It could reflect hydrocortisone or endogenous stress cortisol; there is no treatment data here.",
+    "strength": "artifact-suspect",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_draws.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/programs_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/program_map.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s10_fatal_within_severe.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/fatal_within_severe.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_contrast_agreement.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/transfer_wave_effect.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/attribution_genes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/bulk_baseline_programs.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/s00_explore.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a43c04e51516351c2",
+     "verdict": "qualified",
+     "recomputed": "Own code (v01-v05), CD14 monocyte dex/glucocorticoid score, COVID+ fatal 12 vs survived 46 donors, donor-mean Welch per draw.\n\nReproduction of the claim's numbers:\n- Gene filter (observed CPM >= 10) gives 10/7/9 genes for CD14/CD16/Int, the same as the discovery agent.\n- Discovery-style edgeR logCPM (prior 2), CD14 fatal per draw (0-4): +0.21, +0.36, +0.40, +0.65, +0.52 (p 0.034, 3e-5, 9e-4, 2e-4, 1e-4). Draw-3 group means 6.47 vs 5.82. BH over the discovery agent's 66-test programme family gives q 0.058, 0.001, 0.005, 0.003, 0.001. All exact.\n- Own log2(CPM+1) score: +0.41, +0.63, +0.76, +0.95, +1.13 (all p <= 0.031). The z-scored and linear-scale scores agree in direction.\n- Int +0.24 to +0.47 and CD16 +0.22 to +0.58 reproduce exactly.\n- Wave-1 only: +0.13, +0.30, +0.24, +0.47, +0.45 (p 0.31, 0.016, 0.084, 0.030, 0.023), exact.\n- Within severe/critical (12 vs 16 donors): +0.23 to +0.56 (p <= 0.03), exact.\n- Within severe/critical and wave 1 (7 vs 16): +0.13, +0.28, +0.17, +0.35, +0.42 (p 0.33, 0.022, 0.20, 0.093, 0.030).\n\nConfounders the effect survives (prior2 unless noted):\n- Age, sex and wave: +0.14, +0.34, +0.36, +0.53, +0.50 (all p < 0.05). Fatal donors are not older (70.2 vs 67.9) and the sex mix is similar. Wave 2 is over-represented among fatal donors (5/12 vs 5/46).\n- Dropping the sex_flag sample and training-donor samples: unchanged.\n- Repeated measures: random one sample per donor, MixedLM and donor-clustered OLS all keep 5/5. Earliest sample per donor keeps draws 1-4; draw 0 gives p 0.24.\n- Label confidence: CD14 confidence is balanced between groups. Using subsample cells with confidence >= 0.99 only gives +0.52 to +1.22.\n- Depth: thinning to a common library or a linear-scale score keeps draws 1-4; draw 0 is not significant (p 0.24-0.25).\n\nConfounders that attenuate it:\n- Fatal samples carry 2.3-3.2x more generated CD14 cells (fraction +9 to +15 percentage points; the paired truth shows +2 to +4 points, not significant).\n- WHO severity at the time of sampling (fatal 3.03 vs 1.39): +0.16 (p 0.11), +0.22 (0.015), +0.21 (0.12), +0.46 (0.014), +0.32 (0.011).\n- Within wave 1, after the same adjustment: +0.01 to +0.21, all p >= 0.12. The bulk wave-1 signal drops the same way: +0.92 to +0.49, p 0.10.\n- With every clinical and technical covariate together: -0.10 to +0.13, all p >= 0.06.\n\nCalibration (recomputed on the same restriction):\n- CD14 fatal gate (discovery rule) passes 5/5. The generated/observed ratio is 0.26-0.47 (prior2) or 0.37-0.63 (log2(CPM+1)).\n- Per-sample Spearman generated vs observed 0.53-0.60, against 0.81-0.84 for the bulk score. Partial Spearman given the bulk score is -0.02 to 0.15.\n- Each draw's test set has 7 fatal donors, 6 of them wave 2.\n- Within severe/critical can be calibrated (7 fatal vs 4-5 surviving severe donors). Observed contrast +0.78 to +1.03; generated +0.09 to +0.23, and -0.03 in draw 4. Prior2 ratio -0.02 to 0.17; gates SIGN-ONLY or UNINFORMATIVE, so no draw passes.\n- calibration.csv has no programme rows and no fatal contrast. Its CD14-fraction severity contrast agrees in sign in 3/5 draws, with observed difference about 0.\n\nObserved paired cells:\n- Fatal +0.94 (p 5e-4, 8 vs 26 donors), severity +0.70 (p 0.004), wave-1 severity +0.46 (p 0.17), within severe/critical +0.74 (p 0.073). All match the claim.\n- Adjusted for severity at sample plus wave: +0.53 (p 0.10).\n\nBulk:\n- Unselected bulk score of the 10 claim genes: +1.34 (p 9e-5).\n- The discovery agent's 11-gene score (chosen from fatal-up bulk genes): +1.49, wave-1 +1.06, paired +0.79. Matches.\n- Per gene: ADAMTS2 +3.17 (rank 1), IL1R2 +3.02, TPST1 +1.98, SLC1A3 +1.94, CD163 +1.88, FKBP5 +1.52. Matches.\n- Does the generated score add to the bulk (likelihood-ratio p, prior2): 0.94, 0.056, 0.95, 0.47, 0.10. With log2(CPM+1): 0.70, 0.26, 0.74, 0.59, 0.042.\n- Given the bulk score, the generated fatal difference is about 0 in draws 0-3 (p 0.49-0.94); draw 4 is +0.44 (p 0.023).\n\nSpecificity and imprinting:\n- GR-core genes (FKBP5, TSC22D3, ZBTB16) also rise in generated memory CD8 T (+0.21 to +0.63), NK, B and cDC2 cells, and in observed CD8 T (+0.50), memory B (+1.33) and cDC2 (+0.95).\n- Each draw's effect tracks the observed dex score of its fatal training donor (Spearman 0.97, p 0.005, log2(CPM+1); 0.87, p 0.054, prior2; n = 5, and draws 1-2 share one donor). That training donor is wave 2 in every draw.\n- Cross-draw Spearman of per-sample scores is 0.66-0.81, and 0.66-0.74 after removing the bulk score.",
+     "issues": [
+      "The wave-1 ('pre-dexamethasone') rise is not specific to fatal outcome. After adjusting for WHO severity at the sample, the generated fatal effect within wave 1 is +0.01 to +0.21 (all p >= 0.12), and the bulk's own wave-1 effect falls to +0.49 (p 0.10). The signal tracks how ill the patient was at sampling, which fits endogenous stress cortisol or ICU hydrocortisone rather than an outcome-specific programme.",
+      "The claim says no calibration is possible within severe/critical patients. It can be computed on the paired test samples (7 fatal vs 4-5 surviving severe donors), and it fails. The generator reproduces -0.02 to 0.17 of the observed +0.78 to +1.03 contrast (prior2), draw 4 has the wrong sign, and no draw passes. The within-severe bulk-only effects therefore have no calibration support.",
+      "Calibration is weak even where the gate passes (CD14 fatal, 5/5). The generated contrast is compressed to 0.26-0.47 of observed (prior2). Partial Spearman given the bulk score is -0.02 to 0.15, so the per-sample generated value adds nothing to the bulk. The paired fatal truth is a wave-2, dexamethasone-era truth (6 of 7 fatal test donors per draw). The bulk-only bulk contrast (+1.34) is 1.8 times the paired one (+0.76), so applying the calibration means extrapolating.",
+      "Draw 0 is fragile. Its q is 0.058, and it is not significant after depth equalisation, the linear-scale score, the earliest-sample-per-donor analysis, or adjustment for technical covariates or severity at sample. The claim correctly lists draws 1-4 as carrying the result.",
+      "The size of the effect per draw tracks imprinting. It rises with the observed dex-monocyte score of each draw's wave-2 (probably dexamethasone-treated) fatal training donor (Spearman 0.97 / 0.87, n = 5). All draws also share the sampling seed and the bulk input; per-sample scores correlate 0.66-0.74 across draws after removing the bulk score. 5/5 positive draws are not five independent replications.",
+      "'Placed inside monocytes' holds only for the monocyte-restricted genes (CD163, VSIG4, ADAMTS2, SLC1A3), which is what a reference atlas would predict. The canonical glucocorticoid-receptor targets FKBP5, TSC22D3 and ZBTB16 also rise in generated and observed T, NK, B and DC cells, consistent with a body-wide glucocorticoid effect rather than a monocyte-specific state.",
+      "Generated fatal samples contain 2.3-3.2x more CD14 cells (+9 to +15 percentage points; the paired truth shows +2 to +4, not significant). Controlling for cell number and depth weakens the effect and removes draw 0, but draws 1-4 survive.",
+      "With all clinical and technical covariates together (age, sex, wave, severity at sample, days since swab, cell number, library size, label confidence), the CD14 effect is gone in all five draws (-0.10 to +0.13, all p >= 0.06). This likely over-adjusts (severity at sample and cell number sit downstream of the bulk signal), but it shows the fatal-specific part is small. The observed truth also weakens to +0.53 (p 0.10) after adjusting for severity at sample plus wave.",
+      "Minor: 'the paired fatal truth comes from 7 test donors, and 28 of their 31 samples are wave 2' mixes two sets. The full paired fatal set is 8 donors, 31 samples, 28 wave 2. Each draw's test set is 7 donors, 26-28 samples, 23-25 wave 2.",
+      "Beyond bulk: none, as claimed. Likelihood-ratio p is 0.056-0.95 (prior2). With log2(CPM+1), only draw 4 reaches p 0.042, and that draw's calibration partial Spearman is about 0.1, so the extra signal is not shown to be real.",
+      "No paper-archive delta was made. This subagent's writes were restricted to its verify directory, so the coordinator decides whether these analysis files enter the PBISC-Diffusion archive."
+     ],
+     "reasoning": "Reproduction. Using the discovery agent's normalisation (edgeR logCPM, prior 2) on my own pseudobulk scores from pb_l2.npz, I get exactly the claimed effects:\n- CD14, CD16 and intermediate monocyte fatal contrasts;\n- the wave-1 and within-severe contrasts;\n- the drawwise BH q over the same 66-test programme family;\n- the quoted Spearman, partial Spearman, ratio and likelihood-ratio values, and the bulk gene statistics.\n\nMy own log2(CPM+1), z-scored and linear-scale scores give the same direction with larger magnitudes. The two page citations check out: Stephenson 2025 PDF p.11 (7 of 16 patients in the 2021 cohort were steroid-treated; dex-monos only in that cohort) and Gisby 2022 PDF p.2 (8 of 17 wave-2 patients received glucocorticoids).\n\nConfounders. The effect survives:\n- age, sex and wave adjustment;\n- dropping the sex_flag sample and training-donor samples;\n- every repeated-measures treatment (mixed model, donor-clustered errors, one sample per donor);\n- restricting to high-confidence labels.\n\nEqualising depth keeps draws 1-4. Two controls do bite. Generated fatal samples carry 2-3 times more CD14 cells, and controlling for this removes draw 0. WHO severity at the sample removes the wave-1 and within-severe-wave-1 effects in every draw, and in the bulk as well. The fully adjusted model removes everything.\n\nHonesty items:\n1. Paired calibration passes the gate but is weak: compressed 2-4 times, with no per-sample information beyond the bulk. It rests on a wave-2 truth. On the within-severe restriction, which the claim calls uncalibratable, it can be computed and fails.\n2. Replication: 5/5 positive draws, with 1-4 carrying under the gate. The draws share the seed and the bulk input, and each draw's effect size tracks its dex-high fatal training donor.\n3. Observed agreement: the paired observed cells show the fatal and severity contrasts, but almost only in wave 2.\n4. Beyond bulk: nothing.\n\nSo the association is real and reproducible. The cell-level placement and the outcome-specific reading are not supported beyond what the bulk and the training imprint give. That is consistent with the claim's own 'artifact-suspect, adds nothing to bulk' framing, with two corrections: the wave-1 signal is severity at sampling, and the within-severe result fails calibration. Verdict: qualified, not confirmed.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v05_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v05_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v01_scores.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v01_scores.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v01_genes_used.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v_common.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02a_meta.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02a_covariate_balance.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_tests.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_effects.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_tech_balance.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_calib.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_bulk.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_lr.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_family_bh.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_calibration_csv_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v02_training_donors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03_confounders2.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03_confounders2.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03a_sample_models.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03b_depth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03c_label_confidence.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03d_specificity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03e_composition_fatal.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03f_lr_prior2.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03g_bulk_gene_fatal.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v03h_imprinting.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_remaining.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_remaining.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_1_calib_spearman.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_2_sev_adjust_bulk_vs_gen.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_3_observed_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_recompute/v04_4_crossdraw_spearman.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a3293714eff29ac0e",
+     "verdict": "qualified",
+     "recomputed": "My own code (vcommon.py and v01–v06; it does not import the candidate's helpers) reproduces every number in the candidate exactly. I used edgeR log CPM with prior 2 and also log2(CPM+1) as a robustness check. All tests are donor-aware Welch.\n\nBULK-ONLY, GENERATED CD14 PROGRAMME (fatal 12 vs survived 46 donors)\n- Scored on 10 genes. MFGE8 (pooled CPM 9.6) and MRC1 (2.5) fall below the CPM 10 cut-off in CD14 cells.\n- Per draw (d0–d4): +0.21 / +0.36 / +0.40 / +0.65 / +0.52 (p 0.034, 3e-5, 9e-4, 2e-4, 1e-4).\n- Without each draw's training donors: +0.21 / +0.36 / +0.39 / +0.61 / +0.52.\n- Intermediate monocytes +0.24 to +0.47; CD16 monocytes +0.22 to +0.58.\n- With the fatal label taken from the single-cell metadata (10 vs 48 donors): +0.21 / +0.33 / +0.33 / +0.59 / +0.51 (p 0.065, 7e-4, 0.009, 0.003, 0.001).\n\nWAVE 1 ONLY\n- Fatal vs survived (7 vs 41 donors): +0.13 / +0.30 / +0.24 / +0.47 / +0.45 (p 0.31, 0.016, 0.084, 0.030, 0.023).\n- Within severe/critical (7 vs 16): +0.13 / +0.28 / +0.17 / +0.35 / +0.42; only 2 of 5 draws reach p<0.05.\n- Critical-fatal minus critical-survived (7 vs 4): +0.05 / +0.17 / +0.05 / +0.13 / +0.16 (p 0.20–0.80).\n- Critical survivors already carry 41–82% of the fatal patients' rise over mild patients.\n\nCALIBRATION ON EACH DRAW'S PAIRED TEST SAMPLES (CD14, fatal)\n- Gate passes in 5/5 draws.\n- Generated/observed contrast ratio: 0.26 / 0.30 / 0.43 / 0.47 / 0.35 (0.36–0.63 with log2(CPM+1)).\n- Spearman, generated vs observed: 0.53–0.60. Bulk score vs observed: 0.81–0.84. Partial correlation given bulk: −0.03 to 0.15.\n- With the single-cell fatal label: passes 5/5, ratio 0.31–0.52.\n- With confirmed deaths only (single-cell \"yes\"), the observed contrast is uninformative (p 0.11–0.14).\n- In wave 2 the \"fatal\" contrast is the severity contrast: the donor split is identical. Partial correlation given bulk is −0.28 to 0.05.\n- Wave-1 severity: uninformative in 5/5 draws (observed +0.44 to +0.59, p 0.14–0.24).\n\nOBSERVED PAIRED CELLS (CD14)\n- Fatal: +0.94 (8 vs 26 donors, p 0.0005).\n- Single-cell fatal label: +1.00 (p 0.013).\n- Confirmed deaths only: +1.08 (3 vs 29, p 0.13).\n- Wave 2, fatal (= severity): +0.85 (7 vs 8, p 0.024).\n- Wave-1 severity: +0.46 (p 0.17).\n- C21, the only wave-1 fatal paired donor, is +1.1 above the other wave-1 donors (rank 2 of 19; rank 1 is a severe survivor, C123). The generated C21 is only +0.24 to +0.60.\n\nPER GENE, GENERATED CD14 (fatal)\n- These genes carry the score: CD163 +0.70 to +1.29, ADAMTS2 +0.29 to +0.92, FKBP5 +0.49 to +0.97, FPR1 +0.34 to +0.60.\n- These do not rise consistently: TSC22D3 −0.17 / −0.06 / +0.13 / +0.03 / +0.24, and VSIG4 −0.04 / −0.05 / +0.08 / +0.26 / +0.15.\n- In observed cells TSC22D3 does rise (+0.49, p 0.003); VSIG4 +0.39 (p 0.10).\n\nBULK (log2 CPM+1)\n- Bulk-only fatal: ADAMTS2 +3.17, IL1R2 +3.02, TPST1 +1.98, SLC1A3 +1.94, CD163 +1.88, FKBP5 +1.52.\n- 11-gene glucocorticoid score: +1.49; wave 1 +1.06; paired bulk +0.79.\n- Wave-1 fatal, genes that rise: ADAMTS2 +2.37 (p 0.031), IL1R2 +2.73 (0.021), CD163 +1.46 (0.012), FKBP5 +1.19 (0.015), MCEMP1 +1.36 (<0.001), ARG1 +1.69 (0.002).\n- Wave-1 fatal, genes that do not: TSC22D3 +0.13 (0.60), ZBTB16 +0.72 (0.13), PER1 +0.14 (0.55), KLF9 +0.21 (0.26), DDIT4 +0.35 (0.30).\n- Wave-2 fatal: all of the core glucocorticoid targets rise. TSC22D3 +0.98 (0.014), ZBTB16 +2.59 (<0.001), KLF9 +1.11 (0.030), DDIT4 +1.16 (0.045), FKBP5 +2.31 (0.002).\n- Observed wave-1 severity in CD14 cells: ADAMTS2 +1.94 (p 0.033), CD163 +0.84 (0.036), FKBP5 +0.99 (0.035), FPR1 +0.59 (0.003). TSC22D3 −0.08 and ZBTB16 +0.21 are not significant.\n\nADDED VALUE OVER BULK\n- Likelihood-ratio p for adding the generated CD14 score to the bulk score: fatal 0.94 / 0.056 / 0.95 / 0.46 / 0.10; wave 1 0.91 / 0.079 / 0.86 / 0.36 / 0.073.\n\nCELL-TYPE SPREAD OF THE CORE (FKBP5, TSC22D3, ZBTB16)\n- It also rises inside generated non-monocytes: CD56dim NK +0.25 to +0.50, memory CD8 +0.21 to +0.63, naive B +0.21 to +0.64.\n- Observed cells show the same spread: CD14 +0.92, cDC2 +0.92, memory B +0.65, memory CD8 +0.49, NK +0.34.\n\nFATAL LABELS\n- The bulk metadata (Zenodo) marks 7 wave-2 donors as fatal.\n- Gisby 2022 (PDF p.2) says 4 wave-2 patients died.\n- The single-cell metadata labels C101, C146 and C147 \"no\", C108 and C145 \"indirect\", and C141 and C33 \"yes\".\n\nDRAW IMPRINT\n- Each draw's effect size tracks how high its fatal training donor scores in observed CD14 cells: C141 6.45 → +0.21; C101 6.71 → +0.36 and +0.40; C108 7.17 → +0.65; C33 8.01 → +0.52. Spearman 0.87, p 0.054, n = 5.\n\nTRANSFER OF CALIBRATION\n- Bulk-only wave-2 fatal samples are later visits at peak severity (C33 days 11 and 16, C108 days 12 and 16). Their bulk programme score is 1.29 log2 above the paired fatal samples (p<0.001).\n- Bulk-only wave-1 fatal samples sit within the paired range (−0.30, p 0.42).",
+     "issues": [
+      "The paired 'fatal' truth that passes the gate is really a severe-illness contrast from the dexamethasone era. Among paired wave-2 donors, 'fatal' is exactly 'severe/critical': fatal_w2 and severity_w2 give the same +0.85 (7 vs 8 donors). Stephenson 2025 (PDF p.11, printed p.10) says all 2021-cohort patients given glucocorticoids were severe/critical. So the 5/5 gate passes calibrate 'severe/critical while steroids were standard care', not death.",
+      "New finding: the fatal labels disagree between sources. The Zenodo bulk metadata marks 7 wave-2 donors fatal. Gisby 2022 (PDF p.2) says '9 patients had a peak illness severity of severe/critical (of whom 4 died)'. The Stephenson single-cell metadata labels C101, C146 and C147 'no' and C108 and C145 'indirect'. C101, the 'fatal' training donor of draws 1 and 2, has a recovery sample at about day 54. The effect survives relabelling (bulk-only +0.21 to +0.59; gate still 5/5, ratio 0.31–0.52). With confirmed deaths only, the observed contrast is uninformative (p 0.11–0.14).",
+      "The gene-level wording is inaccurate. In generated CD14 cells TSC22D3 (−0.17 to +0.24) and VSIG4 (−0.05 to +0.26) do not rise consistently, and MFGE8 and MRC1 are not expressed. The score is carried by CD163, ADAMTS2, FKBP5 and FPR1. TSC22D3 does rise in observed cells (+0.49, p 0.003), so the generator misses the most typical glucocorticoid target.",
+      "The data do not support calling the wave-1 signal a 'glucocorticoid response'. In wave-1 fatal bulk, the core glucocorticoid targets do not rise: TSC22D3 +0.13 (p 0.60), ZBTB16 +0.72 (p 0.13), PER1, KLF9 and DDIT4 all not significant. All of them rise in wave 2. The wave-1 signal is CD163, ADAMTS2, FKBP5 and IL1R2, together with severity / MS1-like genes (MCEMP1 +1.36, ARG1 +1.69, S100A12). Observed wave-1 severity in CD14 cells shows the same pattern. Stephenson 2025 (PDF p.14, printed p.13) says dex-monocytes 'were not observed in any patients in the 2020 Cohort'.",
+      "In wave 1, 'fatal' mostly means 'critical illness'. All 9 wave-1 deaths had peak WHO 'critical' (Zenodo metadata). Generated critical survivors already carry 41–82% of the fatal rise. Critical-fatal minus critical-survived is only +0.05 to +0.17 (p 0.20–0.80). Within severe/critical in wave 1, only 2 of 5 draws reach p<0.05 (+0.13 to +0.42).",
+      "Wave 1 cannot be calibrated. There is one fatal paired donor (C21). Its observed CD14 score is +1.1, but a severe survivor (C123) scores higher, and the generated C21 is only +0.24 to +0.60. The wave-1 severity calibration is uninformative in 5/5 draws (observed p 0.14–0.24).",
+      "The model shrinks the contrast. The generated effect is 0.26–0.47x of observed (0.36–0.63x with log2 CPM+1). Draw effect sizes track the fatal training donor's own monocyte score (Spearman 0.87, p 0.054, n = 5), so the size is partly a property of which donor trained the draw. All draws share seed 20260927, so 5/5 same sign is not five independent replications.",
+      "The all-sample bulk-only fatal group includes wave-2 late visits at peak severity (C33 days 11 and 16, C108 days 12 and 16). Their bulk programme score is 1.29 log2 above the paired fatal samples (p<0.001), which is outside the calibrated range.",
+      "Confirmed: the generated cells add nothing beyond the bulk. Likelihood-ratio p is 0.06–0.95 for all patients and 0.07–0.91 for wave 1. Partial Spearman given bulk is −0.03 to 0.15 (wave 2: −0.28 to 0.05).",
+      "'Inside monocytes' follows from the gene choice: CD163, ADAMTS2 and VSIG4 are monocyte genes. The core glucocorticoid targets (FKBP5, TSC22D3, ZBTB16) also rise within generated NK, CD8 and B cells, as they do in observed cells.",
+      "The biology has precedent. COMBAT 2022 (PDF p.8, printed p.922): a severe-COVID classical-monocyte module 'including the stress response gene FKBP5 and scavenger receptor CD163'. Bernardes 2020 (PDF p.15, printed p.1308; Fig. 7D on PDF p.16): fatality-associated transcripts including TSC22D3, PER1, IRS2, ADAMTS2 and TPST1, 'mostly specific for monocytes'. Stephenson 2025 (PDF p.6, printed p.5): CD163 rises with severity. Tan et al. 2020, Lancet Diabetes Endocrinol 8:659, by Imperial College authors: high cortisol goes with COVID-19 mortality (checked at abstract level on the web; not in the local folder). Knoll 2024, Cell 187:4318, summarised in Stephenson PDF p.14. What is new is only a restatement for this cohort.",
+      "The candidate's own citations are correct: Stephenson 2025 PDF p.11 (dex-monocytes in the 2021 cohort only) and Gisby 2022 PDF p.2 (8 of 17 wave-2 patients received glucocorticoids)."
+     ],
+     "reasoning": "Every number in the candidate reproduces. Nothing in the calibration or the observed cells contradicts the direction of the claim, so it is not refuted. It does not reach 'confirmed', because the calibration, the observed cells and the biology each support only part of it.\n\n1. Calibration. The pre-registered gate passes in 5/5 draws, and still passes with the corrected single-cell fatal labels, but the generated contrast is strongly shrunk (0.26–0.47x). The paired 'fatal' truth is almost all wave-2 donors, where fatal is exactly severe/critical and the steroid-treated patients were all severe/critical, so it calibrates severe illness under steroids, not death. Three of the 7 wave-2 'fatal' donors are 'no' in the single-cell metadata, which matches Gisby's count of 4 wave-2 deaths. With confirmed deaths only, no calibration is possible, and none is possible for wave 1.\n\n2. Observed cells. The association is real in paired cells (+0.94) but is driven by wave 2. Wave 1 has a single fatal donor.\n\n3. Biology. The candidate's claim and its own caveat (confounded by treatment) are fair. But its wording 'plus TSC22D3 and VSIG4 rise' fails at the gene level. Its claim of a 'glucocorticoid response in wave-1 fatal patients' is not supported: the core glucocorticoid targets stay flat in wave 1 and rise only in wave 2, and the wave-1 rise is mostly shared with critical survivors. That makes it a critical-illness monocyte programme (CD163, ADAMTS2, FKBP5, IL1R2, MS1-like genes) more than a fatality-specific glucocorticoid signature. COMBAT, Bernardes, Stephenson's severity results and Tan 2020 show this biology is already expected.\n\n4. Value beyond bulk. None, which I confirmed independently.\n\nDraws carrying the result: CD14 fatal p<0.05 in draws 0–4 (drawwise BH q<0.05 only in draws 1–4). Wave 1: draws 1, 3 and 4. Wave 1 within severe/critical: draws 1 and 4.\n\nProcess: CPU only, all runs capped by systemd-run (6G or less), no GPU work. I wrote files only in my verify directory. I made no archive change; the paper-maintenance update belongs to the coordinator.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/vcommon.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v00_fatal_label_crosscheck.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v00_cause_eskd_fatal.txt",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v01_recompute.log",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v01_fatal_labels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v01_bulkonly_tests.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v01_calib.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v02_genes_bulk_donors.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v04_wave1_peak_severity.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v04_wave1_peak_severity.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v04_wave1_peak_severity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v04_wave1_critical_contrast.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/v06_transfer.log",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/lit/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pages.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/lit/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pages.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/lit/COMBAT2022_Blood_atlas_COVID19_severity.pages.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-PROG-6_calib/lit/Bernardes2020_Megakaryocytes_erythroid_plasmablasts_severe_COVID19.pages.txt"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-CLIN-5",
+   "lens": "imp_clinical",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 17,
+    "theme": "artefact (bulk-to-cell mapping)",
+    "synthesis_strength": "artefact (demonstrated for CD14; T part only in draws 2-3); not biology",
+    "reportable_statement": "In bulk-only samples the generated T-cell share falls and the CD14-monocyte share rises with peak severity, WHO grade and fatality in all five draws (T -0.49 to -1.07; CD14 +0.66 to +1.17 logit), but observed paired cells show neither for peak severity. On the same paired test samples the generator over-calls CD14 with severity in wave 1 in all five draws (+1.05 to +1.69). The generator turns the fresh-PBMC bulk's S100A8/9/12 alarmin and LDG/neutrophil RNA into extra classical monocytes and dilutes T cells.",
+    "carrying_draws": {
+     "cellular finding": "none (all observed contrasts null)",
+     "CD14 artefact (wave 1)": "0-4",
+     "T artefact": "2,3"
+    },
+    "honesty": {
+     "calibration": "calibration.csv severity row: T sign 2/5, CD14 3/5; recomputed donor-aware gates NULL or NULL_genfx everywhere; wave-1 COVID CD14 FAIL/OPPOSITE in 5/5 (observed -0.77, p 0.017). Synthesis i01: l1 T COVID NULL_genfx 5/5; l2 CD14 wave-1 COVID FAIL 5/5.",
+     "replication": "5/5 draws; survives every clinical/technical control; depth adjustment weakens draws 1-2 only.",
+     "observed": "Observed peak T -0.06 (p 0.88), CD14 -0.07 (p 0.87). Fatal same sign but n.s. and wave-confounded. Within donors in wave 1 observed CD14 rises with WHO (+0.66/step, p 0.011).",
+     "beyond_bulk": "The bulk shows the signal: S100 score explains nearly all of the CD14 effect, T-cell bulk genes most of the T effect; monocyte-restricted bulk genes (CD14, VCAN, FCN1, CSF1R, MAFB) do not rise with severity."
+    },
+    "verdict_reasons": [
+     "Core artefact conclusion holds; mechanism broader than LDG (alarmins from LDGs and activated monocytes).",
+     "'Generator produces no T/CD14 severity effect on paired tests' is wrong (CD14 5/5 up, significant in draws 2, 4).",
+     "'Observed shows neither' overreaches for fatal and within-donor WHO.",
+     "Memory CD8 and gdT do not fit the LDG pattern.",
+     "calibration.csv severity row includes recovery samples."
+    ],
+    "refuted_subclaims": [
+     "the effect appears only with the LDG-richer bulk-only bulk",
+     "observed shows neither (fatal, within-donor WHO)",
+     "Memory CD8 T and gdT follow the same pattern"
+    ],
+    "new_vs_cohort_papers": "Ingredients documented: Gisby 2022 p.5 (neutrophil/LDG signature in PBMC bulk rising with severity; CIBERSORTx), p.6-7 (flow: little myeloid/lymphoid change), p.18 (fresh-PBMC bulk; LDGs lost on freeze-thaw); Stephenson 2025 p.20 (cryopreserved PBMC, wave-specific thaw protocols), p.6, p.13. The generator artefact is new to this analysis.",
+    "next_steps": [
+     "Adjust every monocyte/T/platelet association for bulk alarmin, LDG and T scores.",
+     "Condition the generator on (or remove) granulocyte/alarmin bulk signal; add an LDG-aware target."
+    ],
+    "key_sources": [
+     "verify/IMP-CLIN-5_recompute/v02_discordance.csv",
+     "verify/IMP-CLIN-5_recompute/v04_B_adjust.csv",
+     "verify/IMP-CLIN-5_recompute/v05_ldg_cell_labels.csv",
+     "verify/IMP-CLIN-5_recompute/v06_genes.csv",
+     "verify/IMP-CLIN-5_calib/v01_gates.csv",
+     "verify/IMP-CLIN-5_calib/v02_B_adjusted.csv",
+     "verify/IMP-CLIN-5_calib/v02_C_delta.csv",
+     "verify/IMP-CLIN-5_calib/v05_within_donor_loo.csv",
+     "synthesis_imperial_eskd/i01_codes_by_feature.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-CLIN-5",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only samples the generated T-cell share falls and the CD14-monocyte share rises with peak severity, WHO severity and fatality in 5/5 draws. Observed paired cells show neither. The generated shifts track the low-density-granulocyte (neutrophil) signal of the fresh-PBMC bulk, which the cryopreserved single cells lack, so this is a bulk-to-cell mapping artifact and not a cellular finding.",
+    "kind": "other",
+    "feature": "l1 | T cell and l2 | CD14 monocyte. Same pattern: l2 | Memory CD8 T cell ↓ and l2 | gdT ↓ with severity and in wave-1 fatal patients.",
+    "comparison": "Peak severity, WHO at sample and fatal vs survived, among COVID+. Also COVID+ vs COVID− in wave 1.",
+    "effect_bulkonly": "T cells, peak, draws 0–4: −0.49, −0.51, −1.07, −0.99, −0.63 logit; severe/mild fraction 0.49/0.59, 0.56/0.67, 0.37/0.57, 0.39/0.56, 0.44/0.53. CD14 monocytes, peak: +0.73, +0.66, +1.17, +0.98, +1.04; 0.13/0.07 to 0.21/0.08, 1.8–2.5×. Fatal: T −0.52 to −1.29, CD14 +0.91 to +1.39. Wave-1 COVID+ vs COVID−, CD14: +0.14 to +1.01.",
+    "stats": "Model: MixedLM logit ~ x + age/10 + male + d + d² + (1|donor), n = 143 samples from 58 donors. T peak q ≤ 3.1e-3 and CD14 peak q ≤ 0.038 in all draws. Robust to S1–S7 and ethnicity + diabetes. Bulk scores, same model: LDG/neutrophil score (ELANE, MPO, CTSG, CAMP, LCN2, MMP9, CSF3R, CXCR2, FCGR3B) peak +1.02 SD (p 1.2e-6), WHO +0.35 (p 2e-4), fatal +1.06 (p 1.5e-4). T-cell bulk score peak −0.79 SD (p 0.0025). CD14 bulk score (S100A8/9/12 …) peak +1.01 SD (p 5e-6).",
+    "draws_same_sign": "5/5, but no draw carries the result: every gate is NULL_ok or NULL_genfx because the observed contrast is null. CD14 in wave 1 COVID+ vs COVID− is FAIL in 4/5.",
+    "paired_calibration": "calibration.csv, severe vs mild row: T cells same sign 2/5 (gen −0.015/−0.004/−0.076/−0.128/+0.020 vs obs +0.004/+0.006/−0.027/+0.008/+0.013; gates FAIL, FAIL, DIR, FAIL, PASS); CD14 3/5 (gen +0.022 to +0.060 vs obs −0.009 to +0.008). COVID+ vs COVID− row: same sign 5/5 for both, using raw means that ignore donors and wave. Recomputed donor-aware: peak gates all NULL (observed p ≥ 0.4). Wave-1 COVID CD14: observed −0.77 (p 0.017) vs generated positive, FAIL.",
+    "paired_observed_concordance": "No. Observed peak severity: T −0.06 (p 0.88), CD14 −0.07 (p 0.87). Wave 1: T −0.08 (p 0.79), CD14 −0.85 (p 0.10). Fatal: T −0.54 (p 0.23). WHO: T −0.04 (p 0.67). In wave-1 COVID+ vs COVID−, observed CD14 goes the other way: −0.77, p 0.017.",
+    "beyond_bulk": "The bulk already shows the signal behind this artifact, so the generated cells mis-attribute it rather than add to it. The generated CD14 fraction correlates with the bulk LDG score (ρ 0.45–0.52 in bulk-only, 0.51–0.59 in paired generated) but the observed CD14 fraction does not (0.08). The generated T fraction is anti-correlated with LDG (−0.35 to −0.48) while the observed one is not (+0.15). Bulk-only severe/critical samples carry more LDG signal than paired severe samples (+0.31, p 0.008), so calibration does not transfer. On paired test samples the generator itself produces no T or CD14 severity effect (except draw 3), which shows the effect appears only with the more LDG-rich bulk-only bulk.",
+    "novelty": "The mechanism is documented in the cohort papers. Gisby 2022 PDF p. 5: neutrophil/LDG gene signature in PBMC preparations rising with severity; CIBERSORTx gives lymphoid fractions down and myeloid up with severity. Gisby 2022 PDF p. 18: bulk RNA came from fresh PBMC, and LDGs do not survive freeze–thaw. Stephenson 2025 PDF p. 20: CITE-seq used cryopreserved PBMC with dead-cell removal and different thaw protocols in the 2020 and 2021 waves. The artifact itself, a generator turning bulk LDG signal into monocytes, is new to this analysis.",
+    "strength": "artifact-suspect",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_beyond_bulk_checks.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_beyond_bulk_checks.log",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_ldg.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_transfer.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s09_markers.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s10_bulk_vs_gen.parquet",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s13_extra_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s15_findings_numbers.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s19_observed_means.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/gisby2022_text.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/stephenson2025_text.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "af4fc08e5c5272163",
+     "verdict": "qualified",
+     "recomputed": "All numbers below come from my own code, run from the raw compact files. They are collected in v07_quoted_numbers.txt.\n\n**Bulk-only COVID+ samples (143 samples, 58 donors).** Model per draw: LMM, empirical logit ~ x + age/10 + sex + t + t² + (1|donor).\n- Peak severity, T cells by draw 0–4: −0.49, −0.51, −1.07, −0.99, −0.63 (p 1e-6 to 1e-3; BH q ≤ 3.1e-3 across 50 features).\n- Peak severity, CD14 monocytes: +0.73, +0.66, +1.17, +0.98, +1.04 (q ≤ 0.038).\n- Fractions severe/mild:\n  - T: 0.49/0.59, 0.56/0.67, 0.37/0.57, 0.39/0.56, 0.44/0.53.\n  - CD14: 0.134/0.073, 0.129/0.066, 0.197/0.092, 0.209/0.084, 0.115/0.057 (1.8–2.5×).\n- WHO severity: T −0.17 to −0.37; CD14 +0.33 to +0.47.\n- Fatal: T −0.52 to −1.29; CD14 +0.91 to +1.39.\n- Every effect has the same sign in 5/5 draws, and every number the claim states matches my recompute.\n- The effects survive each of these controls:\n  - adding wave as a covariate, or using wave-1 samples only;\n  - dropping the sex_flag sample;\n  - dropping samples whose donor trained the draw;\n  - adjusting for log bulk library size, or for ethnicity and diabetes;\n  - all of the above combined;\n  - GEE, donor-mean analyses, and donors with a single sample (n 17);\n  - high-confidence labels only (confidence ≥ 0.9).\n- Generated cell number is fixed at 1,500 per sample, so it cannot confound.\n- Adjusting for the median generated library size removes the CD14 effect in draws 1–2. It persists in draws 0, 3 and 4.\n\n**Observed paired COVID+ cells (113 samples, 35 donors).**\n- Peak severity: T −0.06 [−0.87, 0.75], p 0.88; CD14 −0.07 [−0.91, 0.77], p 0.87.\n- Wave 1 only: T −0.08 (p 0.79); CD14 −0.85 (p 0.097).\n- WHO severity: T −0.04 (p 0.67) over both waves, but −0.17 (p 0.068) in wave 1.\n- Fatal: T −0.54 (p 0.23) and CD14 +0.60 (p 0.20). Both have the same sign as the generated effect, and both flip after wave adjustment.\n\n**Generator on the same paired TEST samples, peak severity.**\n- CD14: +0.42, +0.50, +1.06, +0.52, +0.53 (p 0.08, 0.2, 0.004, 0.3, 0.005).\n- T: −0.17, −0.13, −0.72 (p 0.05), −0.89 (p 0.002), −0.13.\n- Wave-1 T: −0.37, −0.02, −0.90, −1.08, −0.47, significant in 4/5 draws.\n\n**Same-sample discordance (generated minus observed), CD14.**\n- Wave 1: +1.34, +1.05, +1.69, +1.43, +1.69 (p 3e-5 to 0.04), significant in 5/5 draws.\n- All waves with a wave covariate: +0.68, +0.83, +1.37, +0.83, +1.02, significant in 4/5 draws.\n- The same discordance for T is significant only in draw 3 (and in draw 2 with the wave covariate).\n\n**Calibration, COVID+ test samples only.**\n- Severe minus mild, T: generated −0.027, −0.020, −0.119, −0.165, +0.007 vs observed +0.015, +0.013, −0.025, +0.016, +0.027. Same sign in 2/5 draws.\n- Severe minus mild, CD14: generated +0.029, +0.048, +0.082, +0.037, +0.032 vs observed +0.002, −0.006, −0.007, +0.011, +0.003. Same sign in 3/5 draws.\n- Wave 1: same sign in 5/5 draws for both types, but the observed CD14 differences are ≤ 0.012 against generated differences of 0.05–0.12.\n- In COVID+ wave-1 test samples, generated CD14 is 1.6–2.8× the observed level.\n- Wave-1 COVID+ vs COVID−, CD14: generated +0.09 to +1.02 vs observed −0.68 to −0.86 (p ≤ 0.02). This fails the gate in 5/5 draws.\n\n**Bulk scores and genes (my own gene sets), bulk-only, peak severity.**\n- LDG score +1.02 SD (p 1e-6). WHO +0.35 SD; fatal +1.06 SD.\n- S100 score +1.16 SD.\n- Neutrophil genes: 18/18 rise (median +0.77 SD).\n- T genes: 10/12 fall (median −0.76 SD).\n- Monocyte genes do not rise (median −0.21): CD14 +0.13, VCAN +0.33, FCN1 +0.09, LYZ −0.09. Only CD163 rises.\n\n**Adjusting the bulk-only CD14 severity effect (SD units).**\n- Unadjusted: +0.89, +0.55, +0.83, +0.62, +1.19.\n- For the LDG score: +0.66, +0.22, +0.49, +0.28, +0.85.\n- For the S100 score: +0.27, −0.09, +0.09, −0.02, +0.45.\n- For LDG plus T genes: +0.27, −0.12, +0.19, −0.15, +0.55.\n\n**Paired test samples against bulk scores.**\n- The generated-minus-observed CD14 excess rises with the bulk LDG score: +0.20, +0.41, +0.50, +0.45, +0.19 SD per SD (p ≤ 7e-5 in draws 1–3).\n- Observed and generated CD14 follow the monocyte-specific gene score about equally (observed +0.36; generated +0.22 to +0.45).\n\n**Spearman correlations with the LDG score (reproduced).**\n- Generated CD14: +0.45 to +0.52 in bulk-only, +0.51 to +0.59 in paired generated; observed +0.08.\n- Generated T: −0.35 to −0.48; observed +0.15.\n\n**Neutrophil genes inside generated cells.**\n- Only 8–72 of about 246,000 generated cells per draw meet the LDG rule, against 3,503 of 514,260 observed cells.\n- FCGR3B, CXCR2 and MMP9 are about zero in the generated CD14 pseudobulk.\n- The extra generated CD14 cells are S100A8/9-high classical monocytes.",
+     "issues": [
+      "The claim says the generator gives no T or CD14 severity effect on paired test samples (except draw 3). That is wrong. On those samples, CD14 rises with peak severity in 5/5 draws, significantly in draws 2 and 4 (p 0.004, 0.005). T falls in draw 3 (p 0.002) and draw 2 (p 0.05), and in 4/5 draws within wave 1. The discovery agent's own s15 log marks these draws NULL_genfx. So the statement that 'the effect appears only with the more LDG-rich bulk-only bulk' is not supported: the artifact is a property of the generator on this cohort's bulk.",
+      "Severe bulk-only samples carry more LDG signal than severe paired samples overall (+0.31, p 0.008). Within wave 1 the difference is +0.23 (p 0.1), so it mostly reflects the different wave mix of the two sets.",
+      "Bug in the trust layer. tools/features.py defines the 'severe/critical vs mild/moderate (COVID+)' contrast on severity_peak alone, so the 10 paired recovery samples (all peak severe/critical) fall into the severe group: 49 = 40 COVID+ + 9 recovery in draw 0. The calibration numbers the claim quotes come from this mixed row. Restricting to COVID+ changes the numbers, but the same-sign counts stay at 2/5 (T) and 3/5 (CD14).",
+      "'Observed paired cells show neither' rests on non-significance, and the confidence intervals are wide (±0.8 logit). The observed peak-severity CI contains the bulk-only effect of draws 0, 1 and 4 for T, and of draws 0 and 1 for CD14. The strongest evidence for the artifact is the same-sample discordance, which the claim does not use.",
+      "The observed fatal contrast is uninformative rather than discordant. Observed T −0.54 and CD14 +0.60 have the same sign as the generated effects (not significant). Both flip after wave adjustment, and wave-1 paired data contain only one fatal donor.",
+      "The T-cell half of the claim is weaker than the CD14 half. Bulk T genes fall with severity (10/12 genes), and the generated T fraction mostly tracks them. Observed T falls with WHO severity in wave 1 (−0.17, p 0.068; p 0.01–0.09 in the per-draw test subsets), so real lymphopenia cannot be excluded. The T discordance is significant only in draw 3 (draw 2 with a wave covariate).",
+      "The mechanism is not neutrophil transcriptomes being labelled as monocytes: generated cells almost never express neutrophil genes. The generator converts bulk neutrophil/S100 RNA into extra S100-high classical monocytes plus T dilution. Adjusting for the LDG score removes only part of the CD14 effect (still p < 0.05 in draws 0, 2 and 4). The S100 score and the T-gene (lymphoid dilution) score explain more of it.",
+      "Generated depth also shifts. Generated library size drops with severity in draws 0–2. Adjusting for it removes the CD14 effect in draws 1–2 and the T effect in draw 2; the effects persist in draws 0, 3 and 4.",
+      "The claimed 'same pattern' for Memory CD8 T and gdT is weak. Memory CD8 T is null in draw 0 (−0.10, p 0.5). On paired test samples the generator shows no gdT severity effect and no gdT discordance. Both mainly follow the bulk T-gene score rather than the LDG score. Observed Memory CD8 raw means are higher in severe patients (+0.07 to +0.10).",
+      "Agreement in 5/5 draws is not five independent replications: all draws share the base model, the bulk inputs and the sampling seed. The evidence should rest on the per-draw same-sample discordance.",
+      "Processing protocol confounds the observed COVID+ vs COVID− contrast. Observed CD14 is 3.8% in wave-1 COVID+ samples vs 12.2% in wave-2 COVID+ samples, which used different thaw protocols (Stephenson 2025, PDF p. 20). The 2020 pre-infection controls labelled 'w1|w2' were processed with the 2021 protocol. They drive the observed T increase (+1.11 against them vs +0.11 against 2020-processed controls). The CD14 decrease (−0.77, p 0.017) holds against 2020-processed controls only."
+     ],
+     "reasoning": "**Bottom line:** the claim holds for CD14 monocytes and only partly for T cells.\n\n**Reproduced exactly.** My own code reproduces the bulk-only effect: T falls and CD14 rises with peak severity, WHO severity and fatality, with the same sign in 5/5 draws. Effect sizes, q values and group fractions match the claim.\n\n**Survives the confounders checked.** The effects survive:\n- age, sex and days since swab;\n- wave, as a covariate or with wave-1 only;\n- dropping the sex_flag sample and samples whose donor trained the draw;\n- donor repeated measures (LMM, GEE, donor means and single-sample donors);\n- bulk library size, ethnicity and diabetes;\n- label confidence (cells with confidence ≥ 0.9 only).\nGenerated cell number is fixed at 1,500, so it cannot confound. Adjusting for generated depth weakens draws 1–2 only.\n\n**No draw carries a cellular finding.** The observed paired cells show no peak-severity effect, so every per-draw gate is NULL. I did not try to refute the claim's main conclusion (that this is a mapping artifact). I tested it, and my analyses support it for CD14 more directly than the claim does:\n- On the same paired test samples, the generated-minus-observed CD14 excess grows with severity in wave 1 in all 5 draws.\n- In draws 1–3 this excess scales with the bulk LDG score but not with monocyte-specific genes, and observed and generated CD14 track those genes about equally.\n- In the fresh-PBMC bulk, all 18 neutrophil genes and the S100 genes rise with severity, while the monocyte-abundance genes (CD14, VCAN, FCN1, LYZ) do not. A 1.8–2.5× rise in monocytes is therefore not what the bulk shows.\n\nThe cited paper pages check out:\n- Gisby 2022 p. 5: neutrophil signature and CIBERSORTx myeloid/lymphoid association.\n- Gisby 2022 p. 18: LDGs do not survive freeze–thaw, while the bulk RNA came from fresh PBMC.\n- Stephenson 2025 p. 20: cryopreserved PBMC with dead-cell removal and different thaw protocols by wave.\n\n**Why the verdict is \"qualified\" and not \"confirmed\":**\n1. The supporting sub-claim that the generator gives no severity effect on paired test samples is false. CD14 rises in 5/5 draws (significant in 2), and T falls in 4/5 draws in wave 1. So the effect is not specific to the LDG-richer bulk-only bulk.\n2. The calibration row the claim quotes includes recovery samples (a features.py bug).\n3. For T cells the artifact is less clean. The bulk T genes fall, observed T falls with WHO in wave 1 (borderline), and the T discordance is significant only in draws 2–3.\n4. LDG adjustment explains only part of the CD14 effect. The channel is neutrophil/S100 plus lymphoid dilution, not cells with neutrophil transcriptomes.\n5. The observed fatal contrast cannot decide anything: it has the same sign as the generated effect, is confounded by wave, and wave-1 paired data have one fatal donor.\n6. The Memory CD8 T / gdT extension is weak.\n\n**Draws that carry the artifact evidence:**\n- CD14 discordance: all 5 draws in wave 1; draws 0, 1, 2 and 4 across both waves with a wave covariate.\n- Discordance scaling with the LDG score: draws 1–3.\n- T artifact: draws 2–3 only.\n\n**Scope of this check.** CPU only; no GPU work was started. I wrote only under wf/verify/IMP-CLIN-5_recompute/, so there is no archive delta from this verifier.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v07_quoted_numbers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v07_quoted_numbers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v_common.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v00_explore.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v00_explore.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v01_bulkonly_recompute.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v01_bulkonly.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v01_bulkonly_focal.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v01_bulkonly_all.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v02_paired_observed_and_gen.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v02_paired.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v02_observed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v02_paired_gen.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v02_discordance.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v03_calibration_check.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v03_calibration.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v03_calib_recomputed.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v03_gates.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_ldg_mechanism.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_ldg.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_scores.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_score_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_A_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_B_adjust.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_C_paired.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v04_D_spearman.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v05_cells_confidence_depth_ldgcells.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v05_cells.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v05_per_sample.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v05_tests.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v05_ldg_cell_labels.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v06_extra_confounders.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v06_extra.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v06_extra.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_recompute/v06_genes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/tools/features.py"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a92cb4e3491e9c9ae",
+     "verdict": "qualified",
+     "recomputed": "All numbers below were recomputed independently in v01–v06. Model: logit ~ x + age/10 + male + d + d² + (1|donor), REML, best of 4 optimisers.\n\nBULK-ONLY (reproduced exactly, n 143, 58 donors), draws 0–4:\n- Peak severity, T cell: −0.49, −0.51, −1.07, −0.99, −0.63 (BH over 35 composition features, q ≤ 4e-3).\n- Peak severity, CD14 monocyte: +0.73, +0.66, +1.17, +0.98, +1.04 (q ≤ 0.03).\n- Fatal: T −0.52 to −1.29; CD14 +0.91 to +1.39.\n- WHO: T −0.17 to −0.37; CD14 +0.33 to +0.47.\n- Unchanged after dropping sex_flag samples and draw-trained donors.\n\nDONOR-AWARE PAIRED CALIBRATION (each draw's test rows, n 102, 32 donors), peak severity:\n- T cell:\n  - paired_gen −0.17, −0.13, −0.72, −0.89, −0.13; observed −0.00 to −0.32 (p ≥ 0.4).\n  - Gates NULL, NULL, NULL_GENFX, NULL_GENFX, NULL.\n- CD14 monocyte:\n  - paired_gen +0.42, +0.50, +1.06 (p 0.004), +0.52, +0.53 (p 0.005); observed −0.06 to −0.28.\n  - gen−obs delta +0.55 to +1.13. Gates NULL, NULL, NULL_GENFX, NULL, NULL_GENFX.\n- Wave 1 only, CD14: delta +1.05 to +1.69, p < 0.05 in 5/5 draws.\n- WHO: NULL_GENFX in 5/5 draws for both T and CD14. Generator: T −0.13 to −0.54, CD14 +0.29 to +0.62, all p ≤ 0.05.\n- Fatal: NULL in 5/5 draws.\n- Wave-1 COVID+ vs COVID−, CD14: OPPOSITE in 5/5 draws.\n- No draw carries the T/CD14 severity or fatality effect.\n\nOBSERVED, all paired (n 113, 35 donors):\n- Peak severity: T −0.06 [−0.87, +0.75]; CD14 −0.07 [−0.91, +0.77]. Bulk-only estimates fall outside the CI for T in draws 2–3 and for CD14 in draws 2–4.\n- Wave 1, peak severity: CD14 −0.85 [−1.84, +0.15]; all 5 bulk-only estimates fall outside it.\n- Fatal: T −0.54 [−1.44, +0.35] and CD14 +0.60 [−0.32, +1.52]. These CIs contain every bulk-only estimate. With a wave-2 covariate: T +0.58 (p 0.096), CD14 −0.30 (p 0.48).\n- WHO split into within- and between-donor parts, wave 1:\n  - Within-donor: CD14 +0.66 [+0.15, +1.17], p 0.011, still p < 0.05 in 19 of 20 leave-one-donor-out fits; T −0.18 (p 0.075, fragile).\n  - Between-donor: CD14 −0.54 [−1.24, +0.16], against bulk-only +0.31 to +0.71.\n  - The bulk-only WHO effect is mostly between-donor. Bulk-only within-donor: T −0.03 to −0.14; CD14 +0.12 to +0.34, n.s.\n- Wave-1 COVID+ vs COVID−, CD14: −0.77 [−1.40, −0.14].\n\nBULK SCORES vs peak severity, bulk-only (paired in brackets):\n- Granule/LDG genes (13 genes; called GRAN below): +0.75, p 5e-5 [+0.38, p 0.2].\n- Claim's 9-gene LDG score: +0.70, p 1e-6.\n- S100A8/9/12 (alarmins): +1.03, p 1e-8 [+1.01, p 6e-5].\n- Monocyte-restricted genes (CD14, CSF1R, MAFB, CD300E, VCAN): −0.07, p 0.7 [+0.15, p 0.5].\n- T-cell-specific genes: −0.84, p 2e-3 [−0.46, p 0.1].\n\nBULK-ONLY GENERATED CD14 severity effect after adjustment:\n- + granule score: +0.51 to +0.99 (77–96% kept).\n- + claim's 9-gene LDG score: +0.27 to +0.74.\n- + monocyte-restricted score: unchanged.\n- + alarmin score: +0.22, −0.11, +0.12, −0.04, +0.40.\n- T severity effect + T-specific score: −0.11 to −0.40. Fatal T + T-specific score: about 0.\n\nON TRUTH-KNOWN PAIRED ROWS, CD14 delta (gen−obs) per SD, joint model:\n- Granule score: +0.22 to +0.57, p ≤ 0.03 in 5/5 draws.\n- Observed per-cell CD14-monocyte alarmin expression: +0.30 to +0.52, p ≤ 0.04 in 5/5 draws.\n- Generator slope on bulk alarmins is 1.8–4.1× the observed slope.\n\nOTHER:\n- Observed per-cell alarmin in CD14 monocytes rises with severity: +0.52 log2 (p 3e-4).\n- Paired bulk alarmin = observed CD14 share (+0.22) + observed CD14-monocyte alarmin (+0.48) + granule score (+0.53), all p < 1e-4.\n- Bulk-only minus paired, severe/critical stratum, wave-adjusted:\n  - 9-gene LDG score +0.26 (p 0.04); wave 1 only +0.23 (p 0.1).\n  - Alarmin score +0.08 (p 0.6).\n- Generated CD14 monocytes carry almost no granule transcripts (log2 CPM 0.3–0.8 vs 5.0 observed).",
+     "issues": [
+      "The mechanism is only partly LDG. Granulocyte/LDG granule genes explain 5–25% of the bulk-only CD14 severity effect and almost none of the T effect. The S100A8/9/12 (alarmin) bulk signal explains nearly all of the CD14 effect, and T-cell-specific bulk genes explain most of the T effect. On truth-known paired rows, the generator's CD14 over-call tracks two separate things: the LDG score, and the observed per-cell alarmin rise in CD14 monocytes. That rise is a real cell-state change (Stephenson 2025 PDF p.6; Schulte-Schrepping 2020 PDF p.7). So the generator turns alarmin signal from both LDGs and activated monocytes into extra monocyte abundance.",
+      "One sub-claim is wrong: 'On paired test samples the generator itself produces no T or CD14 severity effect (except draw 3)'. Unadjusted paired_gen severity effects are CD14 +0.42 to +1.06 and T −0.13 to −0.89. These are NULL_GENFX for CD14 in draws 2 and 4 and for T in draws 2 and 3, and NULL_GENFX in 5/5 draws for WHO. The quoted numbers look like the bulk-score-adjusted partial effects from s12, described as if unadjusted.",
+      "'Calibration does not transfer because bulk-only bulk is more LDG-rich' is weak. With a wave adjustment, bulk-only severe samples exceed paired severe samples by +0.26 (p 0.04) on the LDG score, and by +0.23 (p 0.1) within wave 1. The alarmin score that drives the generated CD14 does not differ (+0.08, p 0.6). The generator makes the same spurious effect on paired rows.",
+      "'Observed paired cells show neither' is overstated in two places:\n- Fatal is uninformative. Every bulk-only estimate lies inside the observed CI, the unadjusted point estimates go the same way, and the contrast is confounded by wave (28/31 fatal paired samples are wave 2).\n- In wave 1, within donors, observed CD14 rises with WHO at sampling (+0.66 per step, p 0.011, survives leave-one-donor-out in 19/20) and T trends down (−0.18, p 0.075). The bulk-only WHO effect is mostly between-donor, where the observed cells show the opposite trend.",
+      "Memory CD8 T and gdT do not follow the same LDG pattern:\n- In the lens's own s12, LDG adjustment barely changes their bulk-only effects.\n- In wave-1 fatal, my gates carry them: Memory CD8 CARRY in draws 0, 1, 3 and DIR in 2, 4; gdT DIR in 5/5. But this rests on one paired fatal donor in wave 1 (3 samples).\n- Under peak severity they are unsupported (NULL gates, observed +0.22 / +0.16 n.s.). Paired_gen Memory CD8 even goes the other way in draw 0 (+0.37, p 0.01).",
+      "The claim's 'CD14 bulk score' (S100A8/9/12, MCEMP1, RETN, CXCL8, …) is mostly alarmin and neutrophil genes. Monocyte-restricted bulk genes do not rise with severity in either set. Calling it a CD14 score is misleading, although the fact supports the artifact reading.",
+      "Shared noise: all draws use seed 20260927, so 5/5 sign agreement is not five independent replications. The lens estimated a shared-noise share of about 0.01 for these major types, and all draws also share the same bulk input. Per-draw gates are the relevant evidence, and no draw carries the effect.",
+      "Minor: 'robust to S1–S7' holds for direction only. In S7 (donors with single-cell data elsewhere) T and CD14 lose p < 0.05 in 2–3 of 5 draws."
+     ],
+     "reasoning": "The core conclusion holds: the bulk-only T↓ / CD14↑ with severity is a generator artifact, not a cellular finding. Calibration, observed data and biology each support it. The stated mechanism and two sub-claims need correction, so the verdict is qualified.\n\n**1. Calibration.** I recomputed the trust layer donor-aware, per draw, on each draw's test rows.\n- No draw carries the T or CD14 effect for peak severity, WHO or fatality. Gates are NULL or NULL_GENFX throughout.\n- On rows where the cells are known, the generator itself creates the effect: NULL_GENFX in 2/5 draws for peak severity and 5/5 for WHO, and a significant CD14 delta in all 5 draws in wave 1.\n- Wave-1 COVID+ vs COVID− CD14 is OPPOSITE in all 5 draws.\n- The official calibration.csv severe row agrees: same sign in 2/5 draws for T and 3/5 for CD14.\n\n**2. Observed cells.** These support the claim for peak severity and for the between-donor WHO effect.\n- For CD14 in wave 1, every bulk-only estimate lies outside the observed CI.\n- Fatal cannot be tested; the observed CIs include all generated values.\n- One real signal the claim misses: within donors in wave 1, observed CD14 rises with WHO at sampling, robust to leaving out any one donor.\n\n**3. Biology and novelty.** The cohort papers already document every ingredient.\n- Gisby 2022: neutrophil/LDG signature in PBMC bulk rising with severity (PDF p.5, p.12); CIBERSORTx gives lymphoid down and myeloid up (p.5–6); flow cytometry on cryopreserved PBMC shows little myeloid/lymphoid difference between mild/moderate and severe/critical (p.6–7); bulk RNA from fresh PBMC, and LDGs do not survive freeze–thaw (p.18).\n- Stephenson 2025: cryopreserved PBMC with dead-cell removal and different thaw protocols per wave (PDF p.20); the only severity-linked proportion change was more B-ASC, while S100A8/9/12 and MCEMP1 rise inside monocytes (p.6); COVID+ samples have fewer total and CD14 monocytes (p.13); the cell-type annotation has no granulocyte cluster (p.4).\n- Schulte-Schrepping 2020 PDF p.7: low-density neutrophils and S100A-high monocytes appear in fresh PBMC of severe COVID.\n- New here is only the generator's conversion of this signal into monocyte abundance.\n\n**4. Beyond bulk.** The generated features add nothing: bulk alarmin and T-gene scores explain the effects.\n\n**5. Mechanism.** It is broader than LDG alone. Monocyte-restricted bulk genes do not change with severity, so the bulk gives no evidence of extra monocytes. The generator reads the S100A8/9/12 rise, which comes from both LDGs and activated monocytes, as abundance, at 2–4× the observed slope.\n\n**6. Sub-claims that fail.**\n- \"The paired generator produces no effect, so the effect needs LDG-richer bulk-only bulk\" is contradicted.\n- \"Observed shows neither\" overreaches for fatal and for within-donor WHO.\n- Memory CD8 T and gdT do not fit the LDG story.\n\nNo GPU work was run. All writes are under the verifier dir, and there is no archive delta from this verifier.",
+     "files": [
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v01_effects.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v01_effects.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v01_gates.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v01_bulkonly_all35.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v02_ldg_mechanism.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v02_ldg_mechanism.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v02_A_bulk_contrasts.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v02_scores.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v03_state_vs_abundance.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v03_state_vs_abundance.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v03_who_split.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v03_cd14_state.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v03_alarm_source.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v04_overcall_sources.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v04_overcall_sources.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v05_within_donor_loo.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v05_within_donor_loo.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v05_within_donor_loo.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v06_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v06_summary.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v06_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v06_observed_wave_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v00_pdf_pages.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v00_pdf_hits.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-5_calib/v00_pdf_hits.txt",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/SchulteSchrepping2020_Severe_COVID19_dysregulated_myeloid.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Bennett2003_Interferon_granulopoiesis_signatures_SLE_PBMC.pdf"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-LONG-5",
+   "lens": "imp_longitudinal",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 18,
+    "theme": "artefact (platelet trajectory)",
+    "synthesis_strength": "artefact for the steep draws (0, 2, 3; shown directly for draw 0); no evidence of biology; early-severity direction is calibrated",
+    "reportable_statement": "The generated platelet share of bulk-only wave-1 COVID+ samples falls over days (-0.112, -0.031, -0.063, -0.059, -0.035 logit/day; q < 0.03 in draws 0, 2, 3) while observed paired platelets are flat (+0.006/day in wave 1). No draw passes a wave-1 time gate (levels 2.4-19x too high). The decline follows the bulk ISG and granulocyte scores, which the generator over-weights; adjusting for them removes it in all draws.",
+    "carrying_draws": {
+     "trajectory as biology": "none",
+     "artefact shown on identical paired samples": "0",
+     "within the bulk-predicted range": "1,4"
+    },
+    "honesty": {
+     "calibration": "No draw passes a wave-1 time gate; generated paired-test wave-1 slopes -0.020 to -0.068 where observed is flat; generated-minus-observed significant only in draw 0 (-0.080/day, p 0.0035).",
+     "replication": "5/5 negative; scale-dependent (proportion-scale GLM 2/5 in wave 1); library adjustment removes draw 3.",
+     "observed": "Observed slope +0.005 (all), +0.006 (wave 1), within donors +0.004. Wave-1 observed platelets near floor (median 2 cells per sample; 27% zero), so 'flat' only excludes the steep draws.",
+     "beyond_bulk": "The bulk platelet score itself falls slightly (-0.022 to -0.035 SD/day); a bulk-predicted observed-like slope is -0.012 to -0.017 logit/day (CI -0.054 to +0.023)."
+    },
+    "verdict_reasons": [
+     "'Generator imposes the same decline on paired tests' shown only for draw 0.",
+     "Observed comparator weak (floor, site effect).",
+     "'Not biology' too strong: draws 1, 4 lie within the bulk-predicted range.",
+     "The early-severity direction is calibrated; only its beyond-bulk excess is unsupported (a transfer issue)."
+    ],
+    "refuted_subclaims": [
+     "the early-severity effect is a generator artefact"
+    ],
+    "new_vs_cohort_papers": "Not applicable. Literature reports platelet/MK elevation with severity and persistence (Bernardes 2020 p.5, p.9; COMBAT 2022 p.4).",
+    "next_steps": [
+     "Drop platelet trajectories until wave-1 platelet truth (or site-adjusted truth) exists."
+    ],
+    "key_sources": [
+     "verify/IMP-LONG-5_recompute/v07_key_numbers.txt",
+     "verify/IMP-LONG-5_recompute/v03_paired_test.csv",
+     "verify/IMP-LONG-5_recompute/v05_coupling_diff.csv",
+     "verify/IMP-LONG-5_calib/v05_gate_summary.csv",
+     "verify/IMP-LONG-5_calib/v01_paired_test_time.csv",
+     "verify/IMP-LONG-5_calib/v04_bulk_predicted_slope.csv",
+     "verify/IMP-LONG-5_calib/v03_early_severity.csv",
+     "verify/IMP-LONG-5_calib/v00_observed_by_wave.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-LONG-5",
+    "cohort": "imperial_eskd",
+    "claim": "The generated platelet share of bulk-only donors falls over days: -0.031 to -0.112 logit/day in wave 1, with q < 0.03 in 3 of 5 draws. It is also higher at days 0-7 in severe/critical donors, beyond what the bulk platelet genes explain. Observed paired platelets, however, are flat over time in both waves, and the generator imposes the same decline on paired-test samples. This is a generator artifact, not biology.",
+    "kind": "trajectory",
+    "feature": "l2 | Platelet",
+    "comparison": "Days since first positive swab (0-21 d), wave 1 and all waves. Early (d0-7) severe/critical vs mild/moderate. Bulk-only generated cells vs observed paired cells.",
+    "effect_bulkonly": "Raw window means, d0-7/d8-14/d15-21/>21:\n- d0: 0.033/0.023/0.030/0.019\n- d1: 0.008/0.004/0.007/0.005\n- d2: 0.010/0.005/0.008/0.006\n- d3: 0.004/0.003/0.003/0.001\n- d4: 0.017/0.013/0.021/0.014\n\nSlopes (logit/day):\n- Severity-adjusted: -0.095, -0.028, -0.056, -0.055, -0.021.\n- Wave 1: -0.112, -0.031, -0.063, -0.059, -0.035.\n\nEarly severity effect, wave 1, d0-7: +1.46 to +2.01 logit (p <= 2e-5); +1.33 to +1.64 after adjusting for the bulk platelet score.",
+    "stats": "Model: MixedLM as above.\n- Wave-1 BH q: 7e-5, 0.19, 0.007, 0.026, 0.098.\n- Within-donor FE p: 0.012, 0.43, 0.003, 0.09, 0.28.\n- Early effect: MixedLM logit ~ severity_peak + (1|donor) on wave-1 d0-7 samples (9 vs 14 donors).",
+    "draws_same_sign": "5/5 negative slopes in generated cells; the observed slope is flat.",
+    "paired_calibration": "calibration.csv: ratio 0.51-3.66, Spearman 0.29-0.41; COVID+/- and severity signs 5/5.\nCOVID+ test samples: ratio 0.45-4.24; TIME_PASS in draw 4 only.\nWave 1: platelets are over-generated 2.4-19.4x; W1_TIME_PASS in no draw.\nGenerated paired-test wave-1 slopes are -0.020 to -0.068 where the observed cells are flat.",
+    "paired_observed_concordance": "No for the time trend.\n- Observed slope +0.005/day (p 0.79); wave 1 +0.006 (p 0.82); wave 2 +0.004; within-donor FE +0.004 (p 0.88).\n- Observed early severity effect: wave 1 +0.95 (p 0.13). All waves +1.14 (p 0.003), but only +0.27 (p 0.39) after adjusting for the bulk platelet score.",
+    "beyond_bulk": "The extra signal is not supported. The bulk platelet score (TMEM40, CLEC1B, GP9, ...) falls only slightly in bulk-only donors (-0.035 SD/day, p 0.019) and not in paired donors (p 0.24). The generated early-severity excess survives adjustment for this score, but the observed effect does not.",
+    "novelty": "Not applicable (artifact). Severity-linked platelet and megakaryocyte signals are reported in Gisby 2022 (PDF p.5; PDF p.10: persistent PF4/PPBP in convalescence), but no decline within the acute course.",
+    "strength": "artifact-suspect",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s03_temporal_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s07_adjusted.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s09_shapes.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s13_within_donor.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s15_wave_matched.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_calibration.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s16_wave1_bulkonly.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s18_wave1_early_outcome.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s21_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a314cbb25ebeffa1f",
+     "verdict": "qualified",
+     "recomputed": "All numbers come from my own code, run on cells.parquet, composition.parquet, meta.csv and the raw bulk npz files. Everything is CPU only. Logit models are per draw (0..4) and never pooled across draws.\n\nBULK-ONLY TIME TREND\n- Wave 1, COVID+, 0-21 d (110 samples, 47 donors). Model: LMM elogit ~ days + sev + (1|donor).\n  - Slopes (logit/day): -0.112 (p 1.2e-05), -0.031 (p 0.13), -0.063 (p 0.001), -0.059 (p 0.0062), -0.035 (p 0.05).\n  - BH q over 50 features within each draw: 6.5e-05, 0.19, 0.0077, 0.028, 0.10. So 3 of 5 draws have q < 0.03, which reproduces the claim.\n- All waves (+w2 term): -0.095, -0.028, -0.056, -0.055, -0.021.\n- Within-donor fixed effects, wave 1: -0.164 (p 0.0016), -0.018 (p 0.53), -0.078 (p 0.0013), -0.069 (p 0.039), -0.051 (p 0.062).\n- Per-donor slopes are negative in 22, 17, 23, 18 and 18 of 31 donors.\n- Binomial GLM on proportions (donor-clustered SE), wave 1: -0.054 (p 0.026), -0.036 (p 0.13), -0.049 (p 0.048), -0.045 (p 0.13), -0.017 (p 0.47).\n- Confounder controls that leave the slopes almost unchanged:\n  - +age+sex, +fatal, +generation part/seed;\n  - dropping sex_flag and donor-trained samples: -0.108, -0.039, -0.064, -0.063, -0.035;\n  - no-sc donors only;\n  - label confidence >= 0.99.\n- Adjusting for the generated non-platelet library size makes draw 3 non-significant (-0.035, p 0.12).\n- The decline is concentrated in severe/critical donors:\n  - severe/critical: -0.113, -0.051, -0.073, -0.107, -0.055;\n  - mild/moderate: -0.111, -0.008, -0.054, -0.002, -0.014.\n- Adjusting for the bulk platelet score (canonical 20 genes): -0.097, -0.025, -0.052, -0.054, -0.030.\n- Adjusting for the bulk ISG score: -0.035, +0.009, -0.023, -0.038, -0.010, all p > 0.15.\n- Wave 2 bulk-only (17 samples): +0.010, -0.039, -0.018, -0.016, +0.068.\n- Raw window means reproduce exactly (e.g. draw 0: 0.0329/0.0226/0.0304/0.0192). They are non-monotone.\n\nOBSERVED TRUTH (paired COVID+, 0-21 d), slope with 95% CI\n- Wave 1: +0.006 [-0.046, +0.058].\n- Wave 2: +0.004 [-0.048, +0.055].\n- All waves: +0.005 [-0.031, +0.041]; within-donor FE +0.004.\n- Wave 1 on the proportion scale (GLM): -0.005 [-0.077, +0.067].\n- Wave-1 observed platelets are near the floor: mean share 0.14%, 14 of 51 samples have zero platelets, 28 of 51 have 2 or fewer. Wave 2 mean is 1.41%.\n\nPAIRED-TEST SAMPLES, generated vs observed on the same samples\n- Wave 1 generated slopes: -0.068 (p 0.0075), -0.043, -0.034, -0.034, -0.020.\n- Wave 1, generated minus observed: -0.080 (p 0.0035), -0.038 (p 0.19), -0.044 (p 0.11), -0.023 (p 0.42), -0.030 (p 0.31).\n- Wave 2, generated minus observed: +0.028, -0.005, -0.010, -0.013, +0.026.\n- All waves, generated minus observed: no draw significant.\n- Wave 1 level: ratio 19.4, 6.0, 5.2, 2.4, 10.4; Spearman 0.45-0.60.\n\nMECHANISM (coupling of platelet share to bulk scores)\n- Observed platelets are not coupled to the bulk ISG score: wave 1 -0.10 [-0.45, +0.25]; all waves +0.04 [-0.20, +0.27].\n- Extra ISG coupling in generated vs observed (same samples), wave 1: +0.71 (p 0.00025), +0.27, +0.36, +0.25, +0.29.\n- Extra neutrophil coupling in generated vs observed, wave 1: +0.49, +0.50, +0.45, +0.32, +0.22 (p < 0.05 in 4 of 5 draws).\n- The bulk ISG score falls about 0.1 SD/day in both sets.\n\nCALIBRATION.CSV PLATELET ROWS (re-derived exactly)\n- Ratio: 3.655, 0.709, 0.842, 0.511, 1.632.\n- Spearman: 0.411, 0.375, 0.409, 0.401, 0.294.\n- COVID+/- and severity signs agree in 5 of 5 draws.\n\nEARLY SEVERITY EFFECT (wave 1, d0-7, 9 vs 14 donors)\n- Bulk-only LMM: +2.01, +1.79, +1.61, +1.46, +1.61 (p <= 1.9e-05).\n- Donor-mean Welch test: +2.19, +1.89, +1.78, +1.63, +1.70 (p <= 5e-04).\n- Adjusted for the bulk platelet score: +1.72, +1.67, +1.43, +1.34, +1.47.\n- Adjusted for platelet + neutrophil + ISG scores together: +0.93 to +1.21.\n- Observed truth:\n  - wave 1: +0.95 (p 0.13);\n  - all waves: +1.14 (p 0.0033), which drops to +0.41 (p 0.21) after adjusting for the bulk platelet score.\n- Paired-test, generated minus observed, same samples:\n  - wave 1: +0.92, -0.02, -0.16, -0.10, -0.31, all non-significant;\n  - all waves: +0.60, -0.04, +0.07, +0.22, -0.71.",
+     "issues": [
+      "'The generator imposes the same decline on paired-test samples' is overstated. On the same samples, the generated-minus-observed slope is significant only in draw 0 (wave 1: -0.080/day, p 0.0035; within-donor FE -0.090, p 0.013). Draws 1-4 are negative but not significant (p 0.11-0.42). There is no imposed decline in wave 2 (+0.028, -0.005, -0.010, -0.013, +0.026) or over all waves.",
+      "The wave-1 observed truth is near the detection floor. Platelets are 0.14% on average, 14 of 51 samples have zero and 28 of 51 have 2 or fewer. Wave-1 cells were processed in Newcastle, wave-2 in Cambridge, and wave-1 samples carry about 10x fewer platelets (1.41% in wave 2). On the proportion scale the wave-1 observed CI [-0.077, +0.067] contains every bulk-only GLM slope. On the logit LMM scale, 'flat' excludes only draws 0, 2 and 3. The observed severe/critical wave-1 trajectory rests on 7 donors and 16 samples, while the bulk-only decline lives mainly in that stratum.",
+      "The early-severity part is not shown to be an artifact. The severity sign is calibrated in 5 of 5 draws, both in calibration.csv and in the wave-1 d0-7 restriction. Observed truth points the same way (+0.95 wave 1, +1.30 wave 2, +1.14 all waves). On the same paired-test samples the generator adds no excess (generated minus observed, wave 1: +0.92, -0.02, -0.16, -0.10, -0.31, all non-significant; draw 4 under-generates over all waves, -0.71). The excess beyond the bulk platelet score is seen only in bulk-only samples, cannot be attributed to the generator, and should be called unverified, not an artifact.",
+      "The raw window means quoted as the effect are non-monotone. d15-21 is higher than d8-14 in draws 0, 1, 2 and 4, and in draw 4 d15-21 is the highest window (mean logit -4.28 vs -4.60 at d0-7). The decline is a within-donor effect concentrated in severe/critical donors. In mild/moderate donors, draws 1-4 give -0.008, -0.054, -0.002, -0.014.",
+      "Significance depends on the scale. A binomial GLM on proportions gives p < 0.05 in only 2 of 5 draws in wave 1 and 1 of 5 over all waves. The generated non-platelet library size rises with days in draws 0, 3 and 4, and adjusting for it makes draw 3 non-significant (-0.035, p 0.12).",
+      "The bulk platelet-score decline (-0.035 SD/day, p 0.019) depends on the gene list and the window. My canonical list gives -0.027 (p 0.069); using all days it is non-significant (p 0.37-0.53). The difference between paired and bulk-only is itself not significant. The lens's own s07 shows its score absorbs 35-100% of the generated slope in draws 1, 2 and 4 in a no-sev, all-wave model; the claim text does not mention this.",
+      "The claim omits the mechanism. The bulk-only decline is fully absorbed by the bulk ISG score (adjusted slopes -0.035, +0.009, -0.023, -0.038, -0.010, all non-significant) and partly by the neutrophil score. The generator over-couples platelet share to ISG (5 of 5 draws positive; significant in draw 0) and to neutrophil signal (4 of 5 draws significant); observed platelets show no ISG coupling. This supports the artifact reading. However, ISG is collinear with time, so the artifact conclusion still rests on the thin paired truth. Every draw's training set has severe wave-2 donors (C141, C101, C108, C33) whose observed platelets and ISG move together (Spearman +0.26 to +0.73 within the training rows).",
+      "Agreement in 5 of 5 draws is not independent replication. All draws reuse the same seed per generation part (part 0: 20260927; part 1: 840262485).",
+      "calibration.csv labels its severity contrast 'COVID+' but includes recovery samples (49 vs 62 rather than 40 vs 62 in draw 0). The sign is unchanged.",
+      "Some all-wave early-severity MixedLM p-values are numerically degenerate (e.g. 9.7e-95 in draw 3, 2.4e-30) because most donors have one d0-7 sample. Donor-level Welch tests should be quoted instead (wave 1 p 3.5e-05 to 5e-04)."
+     ],
+     "reasoning": "My verdict is qualified: the core claim holds, but three parts are overstated.\n\n**What reproduces.** The bulk-only trend reproduces exactly from the raw cell tables:\n- wave-1 slopes, BH q-values (3 of 5 draws with q < 0.03) and window means;\n- the observed paired slopes (+0.005, +0.006, +0.004; within-donor FE +0.004);\n- the wave-1 test ratios (2.4-19.4x) and the paired-test generated slopes (-0.020 to -0.068);\n- the early-severity effect (+1.46 to +2.01, about +1.3 to +1.7 after bulk-platelet adjustment).\n\n**What it survives.** Age and sex, fatality, generation seed part, the sex_flag sample, donor-trained exclusion, donors with no single cells, label confidence, and repeated measures (LMM, within-donor FE, per-donor slopes). It is partly scale-dependent: the proportion-scale GLM is significant in 2 of 5 draws, and library-size adjustment makes draw 3 non-significant.\n\n**Gating by each draw's calibration.**\n- No draw passes a temporal calibration in wave 1. Levels are 2.4-19x too high in every draw, and generated slopes are negative where observed slopes are near zero. So no draw carries the trajectory as a finding, which fits the artifact framing.\n- Direct evidence that the generator imposes the decline on the same samples is significant only in draw 0. Draw 0 over-generates platelets (ratio 3.66) and was trained on C141, a severe wave-2 donor with 3-11% observed platelets and high ISG. Draws 1-4 have the same sign but are not significant.\n- The bulk-only decline is significant in draws 0, 2 and 3.\n\n**Mechanism.** I found one that supports the artifact reading. The generated decline is fully absorbed by the bulk ISG score, which falls about 0.1 SD/day. The generator over-couples platelets to ISG and to neutrophil signal, while observed platelets track bulk platelet genes and not ISG. The bulk platelet score explains only about 20% or less of the generated decline in models that include severity.\n\n**Why not confirmed.**\n- The paired-test 'same decline' holds only in wave 1 and is significant only in draw 0.\n- The wave-1 observed truth is near the floor and thin for severe donors, so 'flat' carries limited weight there.\n- The early-severity component is calibrated in sign, agrees in direction with the observed cells, and shows no generated excess on the same paired samples. It should not be called a generator artifact; its beyond-bulk magnitude is unverified.\n\n'Not biology' is better worded as 'no evidence of biology'.\n\nPaper archive: this verifier wrote files only in its own directory and did not run the maintenance update. The coordinator should include this directory in the task-end archive request.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v07_key_numbers.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/vstats.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v01_assemble.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v01_samples.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v01_bulk_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v02_bulkonly_slopes.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v02_bulkonly_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v02_bh_all_features.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v03_paired_truth.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v03_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v03_paired_test.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v03_coupling.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v04_calibration_rederived.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v04_window_means.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v04_early_severity.csv",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v06_list_sensitivity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v06_extras.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v08_transfer.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v08_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_recompute/v07_key_numbers.py"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a0781c37bc7d51fc2",
+     "verdict": "qualified",
+     "recomputed": "All of the candidate's quoted numbers reproduce exactly. I rebuilt them independently from composition.parquet with an LMM on logit share, donor random intercept, ML fit. The draw 0-4 values:\n\nReproduced\n- Bulk-only wave-1 slopes: -0.112, -0.031, -0.063, -0.059, -0.035 per day (p 1e-5, 0.13, 0.001, 0.006, 0.050).\n- Severity-adjusted, all waves: -0.095, -0.028, -0.056, -0.055, -0.021.\n- Observed truth: +0.005/day overall (95% CI -0.031 to +0.041); wave 1 +0.006 (-0.046 to +0.058); wave 2 +0.004.\n- Generated/observed ratios: COVID+ test 4.24, 0.76, 0.90, 0.45, 1.52; wave-1 test 19.4, 6.0, 5.2, 2.4, 10.4.\n- Time gate: passes only in draw 4 (all waves); no draw passes in wave 1.\n- Early severity, bulk-only wave 1: +2.01, +1.79, +1.61, +1.46, +1.61; after the candidate's platelet score +1.64, +1.63, +1.38, +1.33, +1.44.\n- Observed early severity: wave 1 +0.95 (p 0.13); all waves +1.14 (p 0.003), falling to +0.27 (p 0.39) after the bulk platelet score.\n\nNew: direct test on identical paired-test samples, (gen - obs) ~ days + sev + (1|donor)\n- Wave 1: -0.080/day in draw 0 (p 0.0035; within-donor FE p 0.004; donor-slope Wilcoxon p 0.03).\n- Wave 1, draws 1-4: -0.038, -0.044, -0.023, -0.030 (p 0.11 to 0.42).\n- All waves: p 0.22 to 0.94 in every draw. Wave 2: not significant in any draw.\n- Generated paired-test wave-1 slopes: -0.068 (p 0.0075), -0.043, -0.034, -0.034, -0.020 (p 0.09 to 0.39).\n\nNew: how informative the observed data are\n- Observed wave-1 platelets: median 2 cells per sample; 27% of samples have zero.\n- Power of the observed wave-1 data to detect each bulk-only slope: 0.99 (draw 0), 0.21 (draw 1), 0.67 (draw 2), 0.61 (draw 3), 0.26 (draw 4).\n- The source h5ad carries the published QC: minimum nCount_RNA is 1000. The authors' platelets are 2,901 of 588,389 cells, with median 1,478 UMIs.\n- Processing site sets the level. Observed share in COVID- samples is 0.0008 when processed at Newcastle (wave 1) vs 0.0058 for pre-infection samples processed at Cambridge (7x). COVID+ is 0.001-0.002 in wave 1 vs 0.009-0.018 in wave 2.\n\nNew: what the bulk alone predicts\n- The bulk platelet score is a reasonable stand-in for observed single-cell platelets: Spearman 0.42-0.55 within wave.\n- Bulk platelet score slope, wave 1: bulk-only -0.032 SD/day (p 0.036); 20 canonical genes -0.022 (p 0.16); paired donors -0.023 (p 0.27). The bulk-only minus paired difference is -0.006 (p 0.81).\n- Slope a calibrated generator should show in bulk-only wave 1 (observed model y ~ PLT + GRAN + ISG + sev + w2, applied to the bulk-only bulk): -0.012 to -0.017 logit/day, donor-bootstrap 95% CI -0.054 to +0.023.\n- The generated slopes are 9.7x, 2.6x, 5.5x, 5.1x and 3.0x that value. Draws 0, 2 and 3 fall outside the CI; draws 1 and 4 fall inside.\n\nNew: mechanism, on identical paired-test samples\n- Generated minus observed coefficient on the bulk interferon (ISG) score: +0.70 in draw 0 (p 0.0001); +0.21 to +0.34 in draws 1-4 (p 0.06 to 0.23).\n- Same for a bulk granulocyte score: +0.35 to +0.61 (p 0.0001 to 0.076).\n- The observed ISG coefficient is about zero (-0.06 to +0.13). ISG falls about 0.10 SD/day in both donor sets.\n- Adjusting bulk-only slopes for platelet score + ISG + granulocyte score gives -0.013, +0.010, -0.012, -0.037, -0.007 (p 0.12 to 0.71). The platelet score alone leaves -0.097, -0.025, -0.052, -0.054, -0.030.\n\nNew: severity\n- Severity direction holds within each wave, over the whole 0-21 d window:\n  - Observed: wave 1 +1.33 (p 0.0009); wave 2 +1.69 (p 0.0001).\n  - Generated paired-test, all 5 draws: wave 1 +0.89 to +2.38; wave 2 +0.84 to +1.68.\n- Early severity beyond the bulk platelet score, generated minus observed on paired-test samples: -0.57 to +0.69, all p > 0.13. The generator adds no visible excess on calibration samples.\n- Bulk granulocyte score, severe vs mild in early samples: +1.05 SD in bulk-only donors (p 0.0001) vs -0.13 in paired donors (p 0.79).\n- Adjusting for platelet score + ISG + granulocyte score cuts the bulk-only early-severity effect to +0.66 to +0.97 (p 0.007 to 0.06).",
+     "issues": [
+      "Overstated: 'the generator imposes the same decline on paired-test samples'. On identical wave-1 paired-test samples, the generated-minus-observed slope is significant only in draw 0 (-0.080/day, p 0.0035). In draws 1-4 it is -0.023 to -0.044 (p 0.11 to 0.42). Over all waves and in wave 2, no draw differs. Paired-test generated slopes are also smaller than the bulk-only ones (draw 0: -0.068 vs -0.112). So same sign in 5/5 draws, but a demonstrated excess only in draw 0.",
+      "Weak comparator: 'observed flat' rules out only the steep draws. Observed wave-1 platelets sit at a floor (median 2 cells per sample, 27% zero). The power to detect the draw 1 and draw 4 slopes is 21-26%; the 95% CI of the observed slope excludes only draw 0's slope (draws 2-3 sit just outside it). The observed object carries Stephenson 2025's >=1000-UMI QC (PDF p.21); minimum nCount_RNA is 1000 and the authors' platelets have a median of 1,478 UMIs. The level is set by single-cell processing site (Newcastle for wave 1; Cambridge for wave 2 and pre-infection samples; PDF p.20): COVID- samples show 0.0008 vs 0.0058 (7x).",
+      "'Not biology' is too strong. The bulk platelet signal itself falls slightly (bulk-only wave 1 -0.032 SD/day, p 0.036; -0.022, p 0.16 with 20 canonical genes). The paired donors' bulk slope is not different (p 0.81), so 'not in paired donors' reflects power, not a real difference. The bulk-predicted observed-like slope is -0.012 to -0.017 logit/day (CI -0.054 to +0.023). Draws 1 and 4 (-0.031, -0.035) fit inside it; draws 0, 2 and 3 are 3-10x steeper and outside it. The artifact is shown for draw 0 and is likely for draws 2-3. Draws 1 and 4 are unresolved.",
+      "Mechanism the claim misses. The generator ties platelet share to the bulk ISG score (draw 0 p 0.0001; positive but not significant in draws 1-4) and over-weights a granulocyte score (significant in 4 of 5 draws in wave 1). Observed platelets show no ISG dependence. ISG falls steeply with time, so adjusting for platelet score + ISG + granulocyte score removes the generated decline in all 5 draws. A plausible origin is cross-cohort learning: Bernardes 2020 links MK/platelet levels to serum IFN-alpha (PDF p.5), but that link is absent in this cohort's cells.",
+      "Early severity: the claim lumps two different things. The severity direction is calibrated: same sign in all 5 draws in calibration.csv, within wave 1 and within wave 2 for both observed and generated. It also matches the literature, so it is not an artifact. The beyond-bulk excess in bulk-only donors (+1.33 to +1.64) is unsupported, as the claim says. But the generator adds no such excess on paired-test samples (generated minus observed -0.57 to +0.69, not significant). The excess comes from a transfer difference: bulk-only severe early donors have a much higher bulk granulocyte score (+1.05 SD) than paired severe early donors (-0.13), and the generator over-weights that score. Adjusting for ISG and granulocyte score halves the effect (+0.66 to +0.97).",
+      "Draw gating should be stated. No draw passes the wave-1 time gate. The only time-calibrated draw (draw 4, all waves) shows no significant bulk-only decline (wave 1 -0.035, p 0.050, q 0.098). The strongest decline (draw 0) is also the draw that fails the level gate (calibration.csv ratio 3.66; wave-1 ratio 19.4). Draw 0's training donor C141 is a severe wave-2, Cambridge-processed donor with 7.6% observed platelets. The '5/5 negative' agreement shares the seed-20260927 noise, so it is not five independent replications.",
+      "Citation precision. Gisby 2022 PDF p.5 'RUNX1 regulates genes involved in megakaryocyte differentiation and platelet function' is a plasma proteomic severity pathway, not PBMC RNA. On PDF p.10 (Table 1 on p.12), PF4 and PF4V1 pass 1% FDR in convalescence; PPBP does not (BH P 0.016). The literature does not support a steady fall over days 0-21:\n- Bernardes 2020 PDF p.5: MK (platelet) proportions stay elevated throughout the disease course; PDF p.9: the MK module peaks in the critical phase and again in early convalescence.\n- Stephenson 2021 PDF p.2: platelets rise with severity but are not listed among populations that change with symptom duration.\n- COMBAT 2022 PDF p.4: platelet abundance tracks severity, CRP and thromboembolism.\n- Stephenson 2025 (this cohort's single-cell paper) has no platelet result (0 mentions).",
+      "Minor: the candidate's within-donor fixed-effect estimates match mine (-0.138, -0.024, -0.072, -0.064, -0.034). Their p-values (0.012, 0.43, 0.003, 0.09, 0.28) come from a different standard error; my classical-OLS values are smaller. The BH q-values across 51 features were not recomputed, but the underlying per-feature p-values match."
+     ],
+     "reasoning": "I tried to refute the claim that the generated bulk-only platelet decline is a generator artifact, not biology.\n\n**Calibration.** The calibration does not support the time contrast in any draw. No draw passes the wave-1 time gate, and wave-1 levels are over-generated 2.4-19x. The only time-calibrated draw (draw 4, all waves) shows no significant bulk-only decline. On identical paired-test samples, a generator-imposed excess decline is shown only for draw 0. A mechanism supports the artifact reading: the generator ties platelets to the bulk interferon score and over-weights the granulocyte score, both of which fall with time. The observed cells show no interferon dependence, and adjusting for these scores removes the decline in all 5 draws.\n\n**Observed support.** Observed paired platelets are flat, but the comparator is weak. Wave-1 counts sit at a floor because of the published 1000-UMI QC, and the processing site sets the level (a 7x site difference even in COVID- samples). The observed data therefore rule out the steep draws (0, 2 and 3), not the shallow ones (1 and 4).\n\n**Bulk.** The bulk platelet genes themselves fall slightly, and a bulk-only prediction from the observed couplings gives about -0.012 to -0.017 logit/day. That covers draws 1 and 4 and excludes draws 0, 2 and 3.\n\n**Biology and novelty.** The literature reports platelet/MK elevation with severity and persistence or a two-peak pattern over the illness (Bernardes 2020, Stephenson 2021, COMBAT 2022, Gisby 2022). None reports a steady fall over days 0-21. This supports 'not established biology' but cannot exclude a small decline. Novelty is not applicable.\n\n**Early severity.** The severity direction is calibrated and supported by observed data and the literature. The beyond-bulk excess is unsupported, as claimed, but it is a calibration-transfer issue (bulk-only severe donors carry more granulocyte signal), not a bias visible on paired-test samples.\n\n**Verdict: qualified.** The practical conclusion holds: the decline is not a reportable finding, and the steep declines are generator artifacts, demonstrated directly for draw 0. Two parts are overstated: 'imposes the same decline on paired-test samples' and 'not biology'. The artifact label should also be kept apart from the calibrated severity direction.\n\nNo GPU was used. Writes were confined to the verifier directory. The paper archive update (POLICY.md) is left to the coordinating agent: nothing was published from here, so that status is pending, not uploaded.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v05_gate_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v01_paired_test_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v01_truth.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v01_bulkonly_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_bulk_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_adjusted_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_coupling.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_wave_check.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v03_coupling_diff.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v03_early_severity.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v03_severity_by_wave.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v04_bulk_predicted_slope.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v04a_source_qc.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v00_observed_by_wave.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v00_train_donors.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v06_literature_quotes.txt",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/vutil.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v01_time_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v02_bulk.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v03_severity_and_coupling.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v04_bulk_predicted_slope.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/v05_gate_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-5_calib/run.sh"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-POP-5",
+   "lens": "imp_populations",
+   "final": "qualified",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "qualified",
+   "synthesis": {
+    "rank": 19,
+    "theme": "artefact (population)",
+    "synthesis_strength": "artefact (demonstrated); companion clusters NK c15 and Mono c4 are not pure artefacts",
+    "reportable_statement": "A generated lineage-mixed cluster labelled 'T cell' (T r1.0 c1; HLA-DR/DP/DQ+, CD74+, CD79A+, S100A8/9+, low library; 75% of its cells lack CD3E/CD3D/CD2) is over-produced 11-19x, fails calibration in every draw x contrast, and recurs at the same noise slots across draws. It should be excluded from bulk-only analyses. Its clinical rise is mostly the generated T-cell denominator. The two smaller mixed clusters (NK r1.0 c15, Mono r1.0 c4) have observed counterparts that rise with COVID.",
+    "carrying_draws": {
+     "biology": "none",
+     "Mono r1.0 c4 COVID (side cluster)": "3"
+    },
+    "honesty": {
+     "calibration": "0/20 draw x contrast rows pass (ratio 1.9-31; Spearman -0.15 to +0.11), with the cluster label or an embedding-free 'mixed' rule.",
+     "replication": "Same-slot OR median 112 vs 1.7 shifted; 34-67% of cluster cells share slots between draws (shared noise).",
+     "observed": "Observed co-clustered cells 0.17% of T, mostly true T cells; observed contrasts null. A CD3-negative mixed phenotype: 8 cells in 6 COVID+ donors, 0 of 34 COVID- donors (p 0.025), about 16x below generated.",
+     "beyond_bulk": "Not biology; the fraction tracks bulk T-cell loss (Spearman -0.21 to -0.72) and inflammation scores."
+    },
+    "verdict_reasons": [
+     "Clinical rise mostly lymphopenia/denominator: after adjusting for generated T share, fatal 0/5, severity 1/5.",
+     "Side clusters' observed cells rise with COVID (p 0.0045, 0.0008); platelet-gene content suggests aggregates.",
+     "Stephenson 2025 QC removed doublet-enriched clusters (PDF p.e4), so 'no observed counterpart' is partly QC."
+    ],
+    "refuted_subclaims": [
+     "the companion clusters behave the same way",
+     "no observed counterpart at all"
+    ],
+    "new_vs_cohort_papers": "Not biology; matches the over-produced doublet-like clusters of the sepsis run.",
+    "next_steps": [
+     "Filter generated cells lacking lineage genes of their label (e.g. no CD3 in 'T') before analyses."
+    ],
+    "key_sources": [
+     "verify/IMP-POP-5_recompute/tables/v12_verdict_numbers.csv",
+     "verify/IMP-POP-5_recompute/tables/v08_covariates_by_contrast.csv",
+     "verify/IMP-POP-5_recompute/tables/v09_observed_by_group.csv",
+     "verify/IMP-POP-5_recompute/tables/v11_side_tests.csv",
+     "verify/IMP-POP-5_calib/tables/v06_per_draw_summary.csv",
+     "verify/IMP-POP-5_calib/tables/v02a_tgene_by_depth.csv",
+     "verify/IMP-POP-5_calib/tables/v02b_calibration.csv",
+     "verify/IMP-POP-5_calib/tables/v04_shared_noise.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-POP-5",
+    "cohort": "imperial_eskd",
+    "claim": "A generated-only, lineage-mixed cluster labelled 'T cell' (HLA-DR/DP/DQ+, CD74+, CD79A+, low library) rises with COVID, severity, WHO grade and fatal outcome in bulk-only cells in 4/5 draws. It has no observed counterpart, fails calibration in every draw, and most of its cells lack T-cell genes. It is an artefact of the generator, not a population.",
+    "kind": "population",
+    "feature": "T r1.0 c1 (the same cells as r0.5 c3 and r2.0 c21): 3,434 subsample cells, 40% labelled 'DN T cell', library 0.59x of the parent. Of its generated cells, 75% detect none of CD3E/CD3D/CD2 (8% in other generated T cells), 37% detect B genes (2%), 37% myeloid genes (10%) and 15% plasma-cell genes. Two smaller clusters behave the same way: NK r1.0 c15 (NK cells with myeloid genes, 77% vs 15%; COVID+ q<0.05 in 4/5 draws; ratio 1.2-9.5x) and Mono r1.0 c4 (monocytes with NK genes, 90% vs 16%; ratio 1.2-5.8x).",
+    "comparison": "COVID+ vs COVID-; severe vs mild; fatal vs non-fatal; WHO grade (all donor-aware).",
+    "effect_bulkonly": "Share of T cells: bulk-only 0.65-4.6% per draw, paired_gen 0.77-3.4%, observed 0.17%, i.e. 11-19x over-produced. COVID+ vs COVID-: d0 0.016 vs 0.012, d1 0.025 vs 0.012, d2 0.096 vs 0.027, d3 0.010 vs 0.003, d4 0.037 vs 0.011. Severity: e.g. d2 0.157 vs 0.040. Fatal: e.g. d1 0.051 vs 0.018.",
+    "stats": "Within-parent cluster fractions from the 200-cell subsample; donor-level Mann-Whitney per draw; BH across all screened clusters at r1.0. COVID p 0.0000-0.20 (draws 1-4 p<0.05; q 0.001-0.33). Severity p<0.05 in draws 0, 1, 2 and 4. Fatal p<0.01 in draws 0, 1, 2 and 4 (q 0.023-0.073). WHO grade p<=0.017 in 4/5.",
+    "draws_same_sign": "5/5 positive for COVID, fatal and WHO grade; 0/5 draws pass the gate. The cluster is stable within generated cells (Jaccard 0.72 on independent re-embedding), so the artefact is systematic.",
+    "paired_calibration": "Fails in every draw. Per-sample ratio of generated to observed share is 2.4-31; Spearman -0.08 to +0.11; observed contrasts are about 0, so the gate fails 5/5. No calibration.csv row (new feature).",
+    "paired_observed_concordance": "No. 27 observed cells co-cluster (0.17% of observed T cells); 63% of them express T-cell genes and 4% B genes, so they are not the same cells. Observed contrasts: COVID +0.0001 (p=0.36), severity -0.001 (p=0.69), fatal +0.0005 (p=0.94).",
+    "beyond_bulk": "No biology. The fraction tracks bulk inflammation/myeloid signals: Spearman with bulk ISG10-HLA2 0.28-0.57 and with bulk CD163 0.21-0.47. The generator turns these bulk signals into mixed cells labelled as T cells. Downstream users of bulk-only cells should exclude such clusters.",
+    "novelty": "Not a biological population. It matches the over-produced doublet-like clusters of the sepsis run (REPORT_ye2026_sepsis.md, section 3).",
+    "strength": "artifact-suspect",
+    "files": [
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/s11_artefacts.py",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s11_artefact_coexpression.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s11_artefact_bulk_spearman.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s04_screen_r1.0.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s02_profile_T_cell_r1.0.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s05_candidates.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s06_stability.csv"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a6cab7e4f90c74ac2",
+     "verdict": "qualified",
+     "recomputed": "I wrote my own code, worked from the raw compact files, and used CPU only. The discovery's T r1.0 labels were used only to define feature A.\n\n**Cluster profile (reproduced)**\n- 3,434 subsample cells: 1,872 bulk-only, 1,535 paired_gen and 27 observed.\n- Generated cells versus the other generated T cells:\n  - none of CD3E/CD3D/CD2 detected: 74.8% vs 7.7%\n  - B genes: 37.0% vs 1.9%\n  - myeloid genes: 37.1% vs 9.6%\n  - plasma genes: 15.4%\n  - labelled 'DN T cell': 40.3%\n  - library: 0.587x\n  - mean L2 confidence: 0.73 vs 0.93\n- Pooled share of T cells:\n  - bulk-only: 1.37 / 1.68 / 4.58 / 0.65 / 2.72%\n  - paired_gen: 1.34 / 1.43 / 3.37 / 0.77 / 2.81%\n  - observed: 0.17%\n- Overlap with the claimed equivalents: r0.5 c3 Jaccard 0.86, r2.0 c21 Jaccard 0.82.\n- Independent re-clustering (fit on generated cells only, one pooled z-score, PCA 40, Leiden): the best match is r0.6 c4.\n  - Jaccard 0.55; 81% of feature A falls inside it.\n  - Top genes: CD74, HLA-DRA, HLA-DRB1, S100A8, HLA-DPA1, S100A9, CD79A, CST3, LYZ, MS4A1.\n  - Library 0.66x.\n\n**Bulk-only contrasts per draw (d0..d4; within-T fraction; donor-aware Mann-Whitney, WHO by donor-clustered OLS)**\nThese reproduce the claim exactly.\n- COVID: diff 0.0042 / 0.0124 / 0.0693 / 0.0072 / 0.0260; p 0.199 / 0.023 / 0.0010 / 0.0005 / <1e-4.\n- Severity: p 0.049 / 0.018 / 0.0001 / 0.994 / 0.0027.\n- Fatal: p 0.0093 / 0.0022 / 0.0007 / 0.42 / 0.0034.\n- WHO grade: p 0.0069 / 0.017 / <1e-4 / 0.24 / <1e-4.\n- Signs are positive in 5/5 draws for all four contrasts.\n- The same pattern holds for:\n  - my cluster C (COVID p<0.05 in 4/5 draws, severity 5/5, fatal 5/5, WHO 5/5);\n  - the embedding-free rule R1 (\"no CD3 plus a B, myeloid or plasma gene\").\n\n**Controls the effect survives**\n- Dropping the sex_flag sample and training-donor samples: COVID d1-d4; fatal d0, d1, d2, d4; severity d1, d2, d4; WHO d0, d1, d2, d4.\n- Also survives: dropping the 3 donors seen on both sides, wave-1 only, age+sex adjustment, L2 confidence ≥ 0.9, the low-library stratum, and disjoint odd/even slot halves.\n\n**Control it does not survive: T-cell share (lymphopenia and the within-T denominator)**\n- Spearman of the within-T fraction with generated T share: -0.36 / -0.71 / -0.85 / -0.20 / -0.68.\n- Generated T share drops with COVID, severity, fatal and WHO grade in every draw (p≤0.04). In observed cells none of these drops is significant (COVID -0.037, p=0.46; severity -0.037, p=0.43; fatal -0.13, p=0.08).\n- Rank-OLS adjusted for generated T share:\n  - COVID: d2, d3, d4 only\n  - severity: d2 only\n  - fatal: none (coefficients fall 60-90%)\n  - WHO grade: d2, d4\n- Adjusted for the bulk T-cell score of the input (CD3E/D/G, CD2, CD247, LCK, IL7R): COVID d2-d4; severity d2, d4; fatal none; WHO d2, d4.\n- All-cells fraction adjusted for T share: fatal in 0/5 draws, severity in d2 only.\n\n**Paired calibration (my code, each draw's paired TEST samples)**\n- Feature A ratio:\n  - all test samples: 3.6 / 5.2 / 19.9 / 2.4 / 9.2\n  - COVID+ only: 4.6 / 8.3 / 31.3 / 3.1 / 13.8\n- Spearman -0.08 to 0.11.\n- COVID sign agrees in d0, d2, d3; severity sign agrees in 0/5.\n- The gate passes in 0/5 draws for every contrast. It also passes 0/5 for cluster C, for cluster C restricted to the claimed mixed phenotype, and for rules R1 and R2.\n- calibration.csv has no row for this feature. Related rows:\n  - 'l2 | DN T cell': ratio 2.3-3.3, Spearman 0.25-0.33, COVID sign agrees 5/5.\n  - 'l1 | T cell': severity sign agrees in only 2/5.\n\n**Observed truth**\n- Feature A: COVID +0.0001 (p=0.36), severity -0.0011 (p=0.69), fatal +0.0005 (p=0.94). These match the claim.\n- The CD3-negative lineage-mixed phenotype within my cluster:\n  - 12 observed cells, 0.08% of T cells.\n  - 8 cells in 6 COVID+ donors and 0 of 34 COVID- donors; Fisher test on donor presence p=0.025.\n  - Most of these cells are JCHAIN-high and labelled 'DN T cell'.\n  - Generated level is 16x pooled, 3-60x per draw.\n- Mapping every observed T cell onto my cluster by kNN gives 1.18% (only 2.1x), because CD3+ observed T cells with ambient B/myeloid reads land there.\n- Rule levels against thinned observed cells: R1 1.1-4.6x, R2 8-25x.\n\n**Bulk**\n- Spearman of feature A with bulk scores: ISG10-HLA2 0.29-0.57 and CD163 0.21-0.47 (both reproduced); myeloid 0.26-0.58; bulk T-cell score -0.23 to -0.72.\n- Does feature A add to a bulk baseline? Fatal: never (LR p 0.26-0.72). Severity: d2 (p=0.004) and d3 (p=0.037) only.\n- Paired and bulk-only bulk scores do not differ within COVID+ or within COVID- (all p≥0.13), so the failed calibration should carry over to bulk-only samples.\n\n**Shared noise**\n- Bulk-only cells sit at the same sample and slot in every draw.\n- P(feature A in draw k | feature A at that slot in draw j) is 17-76x the base rate (median 34x).\n- Cross-draw Spearman of per-sample fractions: 0.33 on shared slots vs 0.21 on disjoint slots, so about 38% of the agreement comes from shared noise.\n\n**Side clusters (my code, discovery labels)**\n- NK c15:\n  - myeloid genes 77% vs 15%\n  - COVID p<0.05 in d0, d1, d2, d4\n  - ratio 1.2-8.5; gate 0/5\n  - observed truth COVID +0.0049 (p=0.0045); generated and observed signs agree 5/5\n- Mono c4:\n  - NK genes 90% vs 16%\n  - observed truth COVID +0.0062 (p=0.0008)\n  - passes the full gate in draw 3 (ratio 1.16, Spearman 0.29, same sign); there bulk-only COVID+ 0.0122 vs COVID- 0.0015, p=0.0007",
+     "issues": [
+      "Most of the clinical rise comes from T-cell share (lymphopenia and the within-T denominator), not from this cluster. Adjusted for generated T share, fatal holds in 0/5 draws (it was 4/5), severity in d2 only, WHO grade in d2 and d4, and COVID in d2-d4. Adjusting for the bulk T-cell score of the input gives the same picture. The generated T-share drop with severity and fatal outcome is not seen in observed cells ('l1 | T cell' severity sign agrees in only 2/5 draws).",
+      "The draw-agreement evidence ('5/5 positive', '4/5 draws') ignores shared noise. A cell at the same sample and slot is 17-76x more likely to be in the cluster in another draw (median 34x), and about 38% of cross-draw per-sample agreement comes from shared slots. The associations do replicate on disjoint slot halves in d1, d2 and d4.",
+      "'No observed counterpart' is slightly overstated. With an embedding-free phenotype (CD3-negative and lineage-mixed), 8 observed cells appear in 6 COVID+ donors and none in 34 COVID- donors (donor presence p=0.025); most are JCHAIN-high cells labelled 'DN T cell'. This is still about 16x below the generated level (3-60x per draw), and calibration fails.",
+      "The over-production figure depends on the definition: 11-19x with the discovery embedding, 2.1x when observed cells are kNN-mapped onto a generated-only embedding (CD3+ cells with ambient myeloid/B reads land there), 16x when the CD3-negative mixed phenotype is required, 1.1-4.6x for the broad rule R1, and 8-25x for R2 (no CD3, high HLA-II).",
+      "The quoted stability (Jaccard 0.72) comes from the discovery's own pipeline with another seed and HVG count. A truly different embedding gives Jaccard 0.55 (81% of the cells kept). The 'same cells' at r0.5 c3 and r2.0 c21 overlap with Jaccard 0.86 and 0.82, so they are not identical.",
+      "The two side clusters do not behave the same way. NK c15 and Mono c4 both rise with COVID in observed cells (p=0.0045 and p=0.0008), with generated and observed signs agreeing in 5/5 draws. Mono c4 passes the full calibration gate in draw 3, so that draw carries a calibrated COVID result.",
+      "The bulk-driver statement is incomplete. The bulk T-cell (lymphopenia) score correlates as strongly as ISG10-HLA2 or CD163, or more (-0.23 to -0.72). The cluster adds nothing to a bulk baseline for fatal outcome in any draw, and adds for severity in d2 and d3 only.",
+      "The quoted q-values come from the discovery's BH family over the whole cluster screen. I reproduced its p-values exactly but did not rebuild that family."
+     ],
+     "reasoning": "I reproduced every number in the claim with my own code and confirmed that the cluster also appears without the discovery embedding: it shows up in an independent re-clustering and under marker rules. The main conclusion stands and is stronger than stated. Calibration fails in every draw for every definition. The cluster cells come back at the same noise slots across draws, so the generator makes them systematically. Paired and bulk-only bulk profiles match, so the calibration failure should apply to bulk-only samples too. Excluding these cells from bulk-only use is justified.\n\nThe descriptive claim that the cluster rises with severity, fatal outcome and WHO grade in 4/5 draws only partly holds. The rise is robust to:\n- the sex_flag sample and training-donor samples;\n- donors seen on both sides, wave, age and sex;\n- label confidence, library strata and slot halves.\n\nIt is largely a lymphopenia and denominator effect. After adjusting for generated T share or for the bulk T-cell score, fatal outcome holds in 0/5 draws and severity in 1-2/5. COVID holds in d2-d4 and WHO grade in d2 and d4.\n\nThree smaller parts also need qualifying: the evidence from draw agreement, the absent observed counterpart, and the statement that the side clusters behave the same way. Because the effect only partly survives confounder control and calibration fails everywhere, I rate the finding qualified. It is not refuted, because the effect reproduces and its main conclusion survives.\n\nEverything ran CPU-only, each Python run inside a 6 GiB systemd scope. The first re-clustering attempt was killed by the out-of-memory killer inside that scope; the rerun fits PCA on 60k cells and completed. I wrote only under the verify directory, so there is no paper-archive change from this subagent.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-5_recompute/tables/v13_resolution_identity.csv"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a6344391a30b364b0",
+     "verdict": "qualified",
+     "recomputed": "All numbers below were recomputed with my own code in IMP-POP-5_calib. I reused only the candidate's s01 Leiden labels; every test was re-implemented as a donor-aware Mann-Whitney on donor means, with an OLS slope and donor-clustered SE for WHO grade.\n\nIDENTITY\n- T r1.0 c1 is the same set of cells as r0.5 c3 (Jaccard 0.87 in generated cells) and r2.0 c21 (Jaccard 0.82).\n\nSHARE OF T CELLS\n- Bulk-only: 1.37 / 1.68 / 4.58 / 0.65 / 2.72 % (draws 0-4).\n- paired_gen: 1.34 / 1.43 / 3.37 / 0.77 / 2.81 %.\n- Observed: 0.17 %.\n- Library of the c1 cells is 0.56-0.63x the generated parent median.\n\nMISSING T GENES IS NOT A DEPTH EFFECT\n- 74.8 % of generated c1 cells detect none of CD3E/CD3D/CD2. The rate is 7.7 % in other generated T cells and 2.0 % in observed T cells (2.3 % after thinning).\n- At matched depth (1,000-1,500 panel counts), 33 % of c1 cells detect a T gene, against 95 % of other generated T cells and 99 % of observed T cells.\n- B, myeloid and plasma genes: 37 / 37 / 15 % in c1 against 2 / 10 / 2 % in the rest.\n- Single genes: CD79A 23 % vs 0.5 %; MS4A1 19 % vs 0.7 %.\n- Median AIFI L2 confidence is 0.77 for c1 cells against 0.998 for the rest.\n\nBULK-ONLY CONTRASTS (draws 0-4; reproduces the candidate)\n- COVID: p 0.199 / 0.023 / 0.0010 / 0.0005 / 3e-5.\n- Severity: p 0.049 / 0.018 / 1e-4 / 0.99 / 0.0027.\n- Fatal: p 0.0093 / 0.0022 / 0.0007 / 0.42 / 0.0034.\n- WHO grade: p 0.0069 / 0.0174 / <1e-4 / 0.24 / <1e-4.\n- The direction is positive in 5/5 draws for every contrast.\n- Sensitivity (drop sex_flag and trained-donor samples): the results hold, except severity in draw 0, which moves to p 0.054.\n\nSHARE OF ALL CELLS (not only T cells)\n- Severity stays significant only in draws 2 and 4 (draw 0 p 0.17, draw 1 p 0.10, draw 3 negative).\n- COVID stays significant in draws 2-4 only; fatal in draws 0, 1, 2 and 4.\n\nCALIBRATION WITH THE CLUSTER LABEL (paired_gen vs co-clustered observed cells on each draw's test samples)\n- Ratio 2.4-31.3; Spearman -0.08 to +0.11; gate passed in 0 of 20 draw x contrast rows.\n- paired_gen shows a COVID+ rise on the test samples themselves: p 0.031 / 0.011 / 0.0003 / 0.20 / 0.0001.\n- Observed cells on the same samples show none: p 0.22-0.65.\n- Observed severity and WHO differences are negative in all draws.\n\nEMBEDDING-FREE RULE 'mixed' (at least 2 HLA class-II genes, no CD3E/D/G or CD2, at least 1 B, plasma or myeloid gene; observed cells thinned to each draw's depth)\n- Captures c1 cells with precision 0.66 in generated cells.\n- Generated 0.55-3.6 % against observed thinned 0.30-0.47 %: ratio 1.9-12.3; Spearman -0.15 to +0.05; gate passed in 0 of 20 rows.\n- paired_gen COVID p 0.0014-0.022, below 0.05 in 5/5 draws.\n- Observed, all 157 samples:\n\n| Contrast | Observed difference | p |\n|---|---|---|\n| COVID | 0.0000 | 0.56 |\n| Severity | -0.0013 | 0.73 |\n| Fatal | -0.0001 | 0.41 |\n| WHO grade | -0.0005 | 0.64 |\n\nOBSERVED TRUTH WITH THE CO-CLUSTERED LABEL (all 157 samples)\n\n| Contrast | Observed difference | p |\n|---|---|---|\n| COVID | +0.0001 | 0.36 |\n| Severity | -0.0011 | 0.69 |\n| Fatal | +0.0005 | 0.94 |\n| WHO grade | -0.0014 | 0.24 |\n\nBIOLOGY CONTROL: HLA-DR+ ACTIVATED T CELLS\n- The rule (T gene present, HLA-DRA detected, no B/plasma/myeloid gene) calibrates reasonably: ratio 0.48-0.92, Spearman 0.16-0.32.\n- In observed cells it does not rise with severity: -0.018, p 0.17.\n- It captures almost none of the c1 cells (precision 0.005).\n- Adding CD38 (CD38+HLA-DR+ T): observed COVID difference +0.0009, p 0.089.\n\nSHARED NOISE\n- Odds ratio that a slot is c1 in two draws: median 112 for the same slot, 1.74 for a shifted slot.\n- Between draws, 34-67 % of c1 cells sit at the same slots.\n- Per-sample fractions correlate 0.26-0.61 between draws.\n\nBULK\n- The c1 fraction correlates with the bulk T-cell score at Spearman -0.21 to -0.69, ISG-minus-HLA +0.28 to +0.57, CD163 +0.21 to +0.47, neutrophil score +0.18 to +0.45, HLA2 -0.24 to -0.45.\n- paired_gen fractions track the paired bulk the same way (T-cell score down to -0.62). The observed fraction does not (all absolute Spearman values 0.13 or less).\n- The bulk alone carries every contrast in bulk-only donors: ISG-minus-HLA p < 1e-4 / 0.0066 / 0.0004 / < 1e-4 (COVID / severity / fatal / WHO); T-cell score severity p 0.013, fatal p 0.0005.\n- The c1 fraction adds to a 6-score bulk baseline for severity only in draw 2 (likelihood-ratio p 0.0055), the worst-calibrated draw.\n\nTRAINING DONORS\n- Draw-level over-production follows the training donors' observed c1 rate. Draw 3's donors carry 0 %; draw 2's carry 0.9 % against a cohort mean of 0.36 %.\n- Draw 2 still amplifies that rate about 7-9x.\n\nCOMPANION CLUSTERS (observed cells, all 157 samples)\n- NK c15 COVID: +0.0049, p 0.0045 (clustered OLS p 0.063).\n- Mono c4 COVID: +0.0062, p 0.0008 (clustered OLS p 0.013).\n- Generated and observed COVID signs agree in 5/5 draws for both.\n- Mono c4 passes the gate in draw 3 (ratio 1.16, Spearman 0.29); bulk-only COVID p 0.0007 there, q 0.0055.\n- NK c15 bulk-only COVID q is 0.046 / 0.0027 / 0.029 / 0.21 / 0.014.",
+     "issues": [
+      "The two companion clusters do not 'behave the same way' for COVID. Their observed co-clustered cells rise with COVID too: NK r1.0 c15 +0.0049 (p 0.0045) and Mono r1.0 c4 +0.0062 (p 0.0008). This is in the candidate's own s04 truth columns. The COVID sign agrees in 5/5 draws for both, and Mono c4 passes the gate in draw 3 (ratio 1.16, Spearman 0.29; bulk-only q 0.0055). Their over-production is real, but their COVID+ rise is not purely a generator artefact. Their observed cells carry platelet genes (22-27 % vs 4-7 %), which fits aggregate biology (Hottz 2020, Blood 136, p. 1330).",
+      "The within-T fraction overstates the bulk-only effects through a T-cell-depletion (lymphopenia) denominator. As a share of all cells, severity stays significant only in draws 2 and 4 (draw 0 p 0.17, draw 1 p 0.10, draw 3 negative); COVID only in draws 2-4. The strongest bulk correlate of the fraction is a low bulk T-cell score (Spearman -0.21 to -0.69). The claim's 'tracks inflammation/myeloid signals' is true but incomplete.",
+      "The '5/5 positive' draw agreement is not independent replication, and the claim does not say so. A slot that is c1 in one draw is far more likely to be c1 in another (odds ratio median 112 for the same slot vs 1.7 for a shifted slot), and 34-67 % of c1 cells share slots between draws. The artefact is tied to the shared initial noise.",
+      "'No observed counterpart' partly reflects the cohort's quality control. Stephenson 2025 removed cells without a souporcell genotype and cells in doublet-enriched clusters (Cell Genomics 5:100918, p. e4). The same group classes T-cell clusters with B-lineage genes as doublets (Stephenson 2021, Nat Med online Methods, PDF p. 16). This explains why over-production is 11-19x here but 1.7-2.2x for the matching sepsis T r1.0 c10 cluster (observed 2.8 %). A COVID-linked rise in raw doublets or aggregates cannot be tested with this data.",
+      "The draw-level magnitude partly follows the training donors (DATA_GUIDE rule 4): draw 2's donors carry 0.9 % observed c1 cells and draw 3's carry 0 %. The generator then amplifies about 7-9x. The claim does not mention this.",
+      "Minor numbers: WHO grade p is 0.0174 in draw 1 (the claim says <= 0.017). Severity in draw 0 (p 0.049) becomes 0.054 in the sensitivity set.",
+      "The closest calibration.csv rows also fail and would have been worth citing: 'l2 | DN T cell' ratio 2.3-3.3 (over-produced), and 'Cycling activated T (CD38/HLA-DR)' ratio 0.10-0.20 (under-produced)."
+     ],
+     "reasoning": "Verdict: the core claim holds up; the qualification is about scope. T r1.0 c1 is a generator artefact, not a population, and every check supports that. The claim's extension to NK c15 and Mono c4 ('behave the same way') is contradicted by observed data for the COVID contrast. Its within-T and 5/5-draw framing also overstates independence and effect size.\n\n(1) Calibration\n- The feature has no calibration.csv row, so I built the equivalent paired check two ways: the candidate's cluster labels, and an embedding-free 'mixed' marker rule scored against observed cells thinned to each draw's depth.\n- Both fail in all 20 draw x contrast rows: the generated level is 1.9-31x the observed level, and the sample-level Spearman is about 0 (-0.15 to +0.11).\n- paired_gen itself shows a COVID+ rise on the same test samples where observed cells show none. Cluster: p < 0.05 in draws 0, 1, 2 and 4. Rule: all 5 draws. So the bulk-only contrast is made by the generator.\n- No draw carries a reportable result.\n\n(2) Observed support\n- Observed cells show no association for COVID, severity, fatal outcome or WHO grade (co-clustered label and rule; all p >= 0.24). Severity and WHO differences are slightly negative.\n- The 27 co-clustered observed cells are mostly true T cells: CD3E 52 %, CD79A 4 %.\n- The lack of T genes in generated c1 cells is not a depth effect: at matched depth, 33 % detect a T gene against 95-99 % elsewhere.\n\n(3) Biology and novelty\n- No HLA-II+ CD79A+ T population is reported for this cohort.\n- Stephenson 2025 (Cell Genomics 5:100918): antibody-secreting B cells rise with COVID (p. 2) and with severity (p. 5; Fig. 3A legend, BH p 0.00031, p. 6); T-cell subsets fall and lymphopenia is noted (p. 2). Activated T cells (1,927 cells) appear after day 10 (pp. 9-10), and severity-associated pathways are scarce in T cells (p. 13). Doublet-enriched clusters were removed in quality control (p. e4).\n- Stephenson 2021 (Nat Med, online Methods) treats mixed T/B-lineage clusters as doublets.\n- Gisby 2022 (Nat Commun 13:7775): severity modules follow neutrophils and plasma cells and run opposite to lymphocytes (p. 5). Lymphoid share falls and myeloid share rises with severity (p. 6). CD38-high plasmablasts and CD69 on CD4 T cells are reported (p. 7).\n- Mathew 2020 (Science 369:eabc8511, p. 5 of 17): real CD38+HLA-DR+ T-cell activation exists but is heterogeneous. In this cohort, HLA-DR+ T cells defined by markers do not rise with severity in observed cells, and c1 cells are not that phenotype.\n- Novelty: it matches the sepsis report's section 3 doublet-like clusters. The sepsis T r1.0 c10 has the same top genes (CD74, HLA-DRA, S100A8/9, HLA-DP/DQ, LYZ, CST3) and the same DN T label.\n\n(4) Beyond bulk\n- The bulk alone already shows every contrast (ISG-minus-HLA, T-cell and neutrophil scores).\n- The fraction adds to a 6-score bulk baseline only in draw 2, which is the worst-calibrated draw.\n- So nothing beyond the bulk is reportable.\n\nRun notes\n- All runs were CPU-only under systemd-run with MemoryMax 6G, swap 0 and CUDA disabled; no GPU work was started.\n- I wrote only under verify/IMP-POP-5_calib.\n- There is no paper-archive delta from this verifier; the archive update is left to the coordinator.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-POP-5_calib/tables/v05_training_donors.csv",
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+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Stephenson2021_Single_cell_multiomics_COVID19.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Mathew2020_COVID_immunotypes_CD38_HLADR_T_cells.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/rare-disease-states/Hottz2020_Platelet_monocyte_aggregates_tissue_factor_COVID19.pdf",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/REPORT_ye2026_sepsis.md",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s04_screen_r1.0.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/tables/s11_artefact_coexpression.csv"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-CLIN-4",
+   "lens": "imp_clinical",
+   "final": "refuted",
+   "verdict_recompute": "qualified",
+   "verdict_calib": "refuted",
+   "synthesis": {
+    "rank": 20,
+    "theme": "refuted (T-cell subset)",
+    "synthesis_strength": "refuted as a generated-cell finding",
+    "reportable_statement": "Not reportable. The generated drop of 'CD8aa' as a share of all cells (-0.38, -0.59, -0.41 in draws 1, 2, 4) is a T-cell-wide fall; CD8aa per T cell does not fall in any draw. In observed cells the T share is unchanged and the CD8aa share within T falls (-0.73, p 3e-4). The two changes are built differently, and the within-T calibration fails in draws 1 and 3 and is direction-only (0.23-0.30x) in draws 0, 2, 4. What survives is an observed single-cell association (CD8aa share of T falls with peak severity, 35 donors), not a bulk-only discovery.",
+    "carrying_draws": {
+     "CD8aa-specific": "none",
+     "all-cell share (direction)": "1,2,4"
+    },
+    "honesty": {
+     "calibration": "All-cell share gates DIR, DIR, PASS, DIR, DIR (COVID+ only). Within-T gates DIR, FAIL, DIR, FAIL, DIR. calibration.csv T-cell severity sign 2/5.",
+     "replication": "Bulk-only all-cell effect in draws 1, 2, 4 survives standard controls; within T +0.20, -0.16, +0.03, +0.24, -0.08.",
+     "observed": "Observed T share -0.06 (p 0.88); CD8aa per T -0.73 (p 3e-4); CD8aa is the only T subset that falls.",
+     "beyond_bulk": "A canonical bulk T score removes 37-100% of the generated effect; the observed effect barely moves."
+    },
+    "verdict_reasons": [
+     "Calib verifier refuted: observed cells and the trust layer contradict the generated mechanism.",
+     "Recompute verifier qualified: real only as a generated T-share (lymphopenia) signal.",
+     "The 'CD8aa' label marks CD8B+ IL7R+ GZMK+ memory-like CD8 cells (75% CD8B+), not innate-like CD8 alpha-alpha."
+    ],
+    "refuted_subclaims": [
+     "CD8aa-specific depletion in bulk-only cells",
+     "observed cells show the same drop"
+    ],
+    "new_vs_cohort_papers": "Stephenson 2025 PDF p.3 (MAIT, CD4/CD8 CM lower in COVID+); p.6 (only B-ASC change with severity at sampling); MAIT/CD8 EM loss with severity in other cohorts (Stephenson 2021 p.5; COMBAT 2022 pp.8, 12).",
+    "next_steps": [
+     "Revisit only with a within-T calibration row and marker-confirmed CD8aa identity."
+    ],
+    "key_sources": [
+     "verify/IMP-CLIN-4_calib/v04_models.csv",
+     "verify/IMP-CLIN-4_calib/v05_tsubset_controls.csv",
+     "verify/IMP-CLIN-4_calib/v07_withinT_calibration.csv",
+     "verify/IMP-CLIN-4_calib/v08_label_biology.txt",
+     "verify/IMP-CLIN-4_calib/v09_transfer_groups.csv",
+     "verify/IMP-CLIN-4_recompute/v10_perT_strata.txt",
+     "verify/IMP-CLIN-4_recompute/v07_bulk_T.txt"
+    ]
+   },
+   "finding": {
+    "id": "IMP-CLIN-4",
+    "cohort": "imperial_eskd",
+    "claim": "Generated CD8αα T cells (AIFI L2 'CD8aa') are lower in severe/critical than in mild/moderate COVID+ patients, including within wave 1, by −0.34 to −0.59 logit (0.47–0.65× by fraction) in draws 1, 2 and 4; draw 3 is weak and draw 0 shows nothing. Observed cells show the same drop. The COVID+ vs COVID− part of the CD8aa decline is not supported in wave 1.",
+    "kind": "composition",
+    "feature": "l2 | CD8aa (companion: l2 | MAIT, which falls with peak severity in wave 1 in draws 2–4)",
+    "comparison": "Peak severity (severe/critical vs mild/moderate) among COVID+, all samples and wave 1. WHO severity at sample, between donors.",
+    "effect_bulkonly": "Peak-severity logit, draws 0–4: −0.03, −0.38, −0.59, −0.34, −0.41. Severe/mild fraction: 0.0105/0.0135, 0.0062/0.0096, 0.0072/0.0153, 0.0048/0.0077, 0.0112/0.0136. Wave 1: −0.05, −0.34, −0.52, −0.34, −0.38. WHO slope: −0.02, −0.14, −0.17, −0.11, −0.19.",
+    "stats": "Model: MixedLM logit ~ severe_peak + age/10 + male + d + d² + (1|donor). Data: n = 143 samples from 58 donors. Peak severity: p 0.81, 1.2e-4, 2.2e-4, 0.039, 6.8e-5; q 0.84, 4.1e-4, 8.7e-4, 0.089, 1.8e-4. Wave-1 q: 0.8, 4e-3, 5e-3, 0.2, 1e-3. Robust to S1–S5, ethnicity + diabetes, and LDG adjustment (−0.65, −0.66, −0.74 in draws 1, 2, 4). No-single-cell donors: draws 1, 2, 4 p ≤ 0.02.",
+    "draws_same_sign": "Signs are negative in 5/5, but draw 0 is −0.03. Significant in 3/5 (draws 1, 2, 4). Gates pass in all 5, but only draws 1, 2 and 4 carry a significant effect. Shared seed noise explains 10.5% of cross-draw agreement for CD8aa and 18.6% for MAIT.",
+    "paired_calibration": "calibration.csv, severe vs mild row: ratio 0.70/0.46/0.74/0.37/0.68; Spearman 0.46/0.55/0.50/0.32/0.40; gen diff −0.004/−0.003/−0.007/−0.002/−0.002 vs obs −0.008/−0.007/−0.009/−0.009/−0.009; same sign 5/5; gates PASS, DIR, PASS, DIR, DIR. COVID+ vs COVID− row: same sign 5/5; gates DIR, DIR, PASS, DIR, DIR. However, the wave-1 recomputation makes the COVID contrast NULL (observed wave 1 −0.14, p 0.52). Recomputed peak: DIR, DIR, PASS, DIR, DIR (gen −0.17 to −0.53 vs obs −0.57 to −0.76). Wave-1 peak: DIR, DIR, PASS, FAIL, PASS.",
+    "paired_observed_concordance": "Yes for severity. Observed peak −0.66 (p 0.0029; 2.1% vs 1.0%); with a wave covariate −0.55 (p 0.003); wave 1 −0.77 (p 6.7e-4). Observed WHO −0.21 per step (p 0.008). Observed COVID+ vs COVID− is −0.60 overall (p 1.5e-6) but only −0.14 in wave 1 (p 0.52).",
+    "beyond_bulk": "Weakly. The CD8aa bulk marker score (FXYD2, DKK3, PPP1R2C; weak markers) shows a trend only: peak −0.40 SD (p 0.07). It still tracks the observed CD8aa fraction better than the generated cells do (Spearman 0.54–0.61 vs 0.32–0.56). Observed CD8aa falls beyond the bulk score (−0.71 SD, p 0.008). The generator reproduces that beyond-bulk part only in draws 1 and 2 (−0.53 and −0.56, p < 0.05). There is no gain in full-bulk prediction (leave-one-donor-out AUC 0.735–0.748 vs 0.743).",
+    "novelty": "Not reported for this cohort as a CD8αα population. Stephenson 2025 PDF p. 3 reports lower MAIT, central-memory and effector-memory T proportions in COVID+ vs COVID−, but no CD8αα label and no severity effect for innate-like T cells. Depletion of innate-like T cells in severe COVID-19 is established in other cohorts (not checked against a local PDF). The label is chrY-sensitive in the frozen labeller (REPORT_ye2026_sepsis.md, SEP-CLIN-6). Models adjust for sex, but any sex contrast of CD8aa is an artifact.",
+    "strength": "weak",
+    "files": [
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+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s13_extra_summary.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s12_partial_calib.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s15_findings_numbers.txt",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/s14_shared_noise.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a2693153e966fd74e",
+     "verdict": "qualified",
+     "recomputed": "All numbers below were rebuilt from the raw per-cell tables (cells.parquet). Per-sample counts match composition.parquet exactly. The mixed model is my own closed-form REML random-intercept model, and it matches statsmodels MixedLM to 4 decimal places.\n\n1. Bulk-only effect (COVID+, 143 samples / 58 donors). Model: logit(CD8aa / all cells) ~ severe + age/10 + male + d + d² + (1|donor). Draws 0-4:\n   - Coefficient −0.025, −0.385, −0.588, −0.343, −0.412.\n   - p 0.81, 1.1e-4, 2.3e-4, 0.039, 6.8e-5.\n   - BH q across the 50 features: 0.84, 4.1e-4, 8.7e-4, 0.089, 1.8e-4.\n   - Donor-level permutation p: 0.56, 0.0015, 0.0015, 0.062, 0.001.\n   - Severe/mild fraction: 0.0105/0.0135 = 0.77x, 0.0062/0.0096 = 0.64x, 0.0072/0.0153 = 0.47x, 0.0048/0.0077 = 0.63x, 0.0112/0.0136 = 0.82x.\n   - Wave 1: −0.05, −0.34, −0.52, −0.34, −0.38 (q 0.8, 0.004, 0.005, 0.14, 0.0014).\n   - WHO slope: −0.02, −0.14, −0.17, −0.11, −0.19.\n\n2. The effect survives these controls (draws 0-4):\n   - Dropping sex_flag and donor-trained samples: +0.01, −0.39, −0.64, −0.35, −0.41.\n   - Also survives adding a wave covariate, wave 1 without trained/flagged samples, days ≤ 20, day categories, non-fatal donors only, and a donor-mean model.\n   - First sample per donor: −0.03, −0.36, −0.50, −0.18, −0.37.\n   - Only CD8aa calls with confidence ≥ 0.9: −0.04, −0.73, −0.84, −0.36, −0.46.\n   - Men only: +0.07, −0.35, −0.34, −0.09, −0.33.\n   - Donors with no single cells (defined from pairs.csv; 30 donors / 81 samples): −0.26 (p 0.02), −0.49 (0.01), −0.29 (0.008) in draws 1, 2, 4.\n   - Generated cell number is 1,500 for every sample.\n\n3. The effect does not survive normalising to T cells:\n   - Generated T share falls in every draw: −0.49, −0.51, −1.07, −0.99, −0.63 (all p ≤ 0.001).\n   - CD8aa per T cell: +0.20 (p 0.05), −0.16 (0.09), +0.03, +0.24 (0.03), −0.08.\n   - CD8aa per CD8 lineage: +0.07, −0.05, +0.18, +0.48, +0.04.\n   - Adjusted for the logit T share: +0.02, −0.32 (p 0.001), −0.19 (0.09), +0.18, −0.21 (0.05).\n   - In the no-single-cell donors, CD8aa per T cell is +0.36, −0.06, +0.17, +0.35, +0.15.\n   - Adjusted for a 10-gene bulk T score: +0.02, −0.25, −0.27, −0.02, −0.23.\n\n4. Observed cells (paired COVID+, 113 samples / 35 donors):\n   - All cells −0.66 (p 0.0029); wave 1 −0.77 (6.7e-4); WHO −0.21 (0.007).\n   - CD8aa per T cell −0.73 (3e-4); per CD8 lineage −0.77 (0.012); adjusted for T share −0.63 (7e-5).\n   - T share does not change with severity: −0.06 (p 0.88).\n   - COVID+ vs COVID− in wave 1: −0.14 (p 0.52).\n\n5. Calibration:\n   - The calibration.csv rows are reproduced exactly.\n   - Its \"severe vs mild (COVID+)\" row (n 49 vs 62) includes 9 recovery samples. On COVID+ rows only, the gates are DIR, DIR, PASS, DIR, DIR, so draw 0 drops from PASS to DIR.\n   - Model-matched recompute on COVID+ paired test samples, CD8aa / all cells: generated −0.23, −0.52, −0.53, −0.17, −0.24 vs observed −0.57 to −0.76. Gates DIR, DIR, PASS, DIR, DIR; in wave 1, DIR, DIR, PASS, FAIL, PASS.\n   - CD8aa per T cell: generated −0.16, −0.44, +0.30 (draw 3), and −0.21, −0.20 vs observed −0.69 to −0.83. Gates DIR, FAIL, DIR, FAIL, DIR.\n   - T share: the observed contrast is null in every draw's test set. Generated T share falls −0.72 and −0.89 in draws 2 and 3 (NULL_genfx).\n\n6. Shared slot noise explains 5.1% (2–9%) of cross-draw CD8aa count covariance, and 11.8% for MAIT.",
+     "issues": [
+      "The generated drop is not specific to CD8aa. It is general T-cell depletion: generated T share falls −0.49 to −1.07 logit in all 5 draws. CD8aa per T cell does not fall in draws 2 and 4 (+0.03, −0.08), rises in draws 0 and 3 (+0.20, +0.24), and is only marginal in draw 1 (−0.16, p 0.09). CD8aa per CD8 lineage does not fall in any draw. In the 30 no-single-cell donors, CD8aa per T cell is positive in 4 of 5 draws.",
+      "\"Observed cells show the same drop\" is true only for the fraction of all cells. The observed drop happens within T cells (per T −0.73, p 3e-4; per CD8 lineage −0.77) while observed T share is unchanged (−0.06, p 0.88). The generated drop works the other way: T share falls and CD8aa per T cell does not.",
+      "The paired calibration does not reproduce the within-T part in any draw. Generated −0.16, −0.44, −0.21, +0.30, −0.20 vs observed −0.69 to −0.83; gates DIR (0.23x), FAIL (level 0.32), DIR (0.30x), FAIL (wrong sign), DIR (0.28x). The T-share severity contrast that carries the bulk-only effect is not supported by observed PBMC: observed null, generated NULL_genfx in draws 2 and 3.",
+      "Much of the effect comes from the bulk. Imperial bulk is whole blood. In bulk-only samples the bulk T score falls −0.81 SD with severity (p 0.001), and adjusting for it removes about half the effect (residual −0.25, −0.27, −0.23 in draws 1, 2, 4). In paired samples, observed PBMC T share rises 0.35 logit per SD of bulk T score, which predicts only −0.28 logit for the bulk-only severity contrast. Draws 1–3 translate bulk T score into generated T share 1.5–2.7x more steeply (slopes 0.52, 0.96, 0.84 vs 0.35).",
+      "The fraction ratio is misreported. Draw 4 is 0.82x (0.0112/0.0136), not within 0.47–0.65x. Across all samples, the logit range for draws 1, 2, 4 is −0.38 to −0.59; −0.34 is a wave-1 or draw-3 value.",
+      "The official draw-0 PASS depends on 9 recovery samples counted in the \"(COVID+)\" severity row; on COVID+ rows only it is DIR. Draw 0 also passes the official gate yet shows no bulk-only effect (−0.03), so the calibration does not carry over to the bulk-only samples there.",
+      "Paired and bulk-only samples differ within the severe group. Bulk-only severe samples are taken later (median day 15 vs 9) and are sicker (12 of 73 critical at sampling vs 3 of 44 paired). Their bulk T drop is also stronger (−0.81 vs −0.43 SD).",
+      "Sex modifies the effect. The CD8aa / all-cells effect is larger in women in draws 2 and 3 (interaction +0.81, p 0.01–0.02; only 7 severe female donors), and this goes through T share. In men only, draw 3 vanishes (−0.09); draws 1, 2, 4 give −0.35, −0.34, −0.33. The labeller is chrY-sensitive and the generated sex effect is −0.75, −0.38, −0.66, −0.53, +0.13, so any sex contrast is an artefact.",
+      "Draw 2 depends on library size. Severe-sample T cells are shallower (mean T-cell library −282, p 8e-5), and adjusting for it shrinks draw 2 to −0.17 (p 0.1). CD8aa per T cell is unchanged by this adjustment, so depth acts through T share, not through label calls.",
+      "Minor: shared slot noise is small (5% of CD8aa count covariance by my estimator; the claim says 10.5%), but agreement between draws still reflects the same bulk input read by 5 models. The wave-1 q for draw 3 is 0.14, not 0.2.",
+      "Gated conclusion: as CD8aa per all cells, draws 1, 2, 4 carry a direction-level result (model-matched gates DIR, PASS, DIR; only draw 2 PASSes on size, and draw 2 is the draw most driven by lymphopenia). As a CD8αα-specific population change, no draw carries it."
+     ],
+     "reasoning": "I recomputed every number in the claim independently, from the raw per-cell tables, with my own REML mixed model. The headline numbers reproduce exactly: the per-draw coefficients, p, BH q, wave-1 effects, WHO slopes and the official calibration rows. The only exceptions are the misreported draw-4 fraction ratio (0.82x) and the recovery samples hidden in the official \"(COVID+)\" severity row.\n\nThe effect holds up against the confounders I was asked to test: sex_flag samples, samples from training donors, wave, time since swab (window, categories, quadratic), repeated measures (first sample per donor, donor permutation, donor means), fatal donors, label confidence, cell number (fixed at 1,500) and men only. So it is not refuted as a statement about the CD8aa fraction of all generated cells.\n\nWhat breaks it is normalising to T cells:\n- Generated T share falls steeply with severity in every draw, and the generated drop disappears once CD8aa is taken per T cell or per CD8 lineage.\n- In the donors with no single cells, CD8aa per T cell goes the other way.\n- In observed paired cells the drop is the opposite case: CD8aa falls within T cells while T share stays flat.\n- So the observed agreement exists only in the all-cells fraction; the generated and observed changes are built differently.\n- Each draw's own paired calibration of the within-T contrast fails or is direction-only at 0.2–0.3x of the observed size.\n- The generated T-share contrast that drives the effect is not seen in observed PBMC, and it is about half explained by the whole-blood bulk T score.\n\nThe draws differ in library size (draw 2), sex balance (draws 2 and 3), and how sick and how late the bulk-only samples were taken.\n\nThe defensible claim is narrower: generated cells from severe COVID+ bulk-only samples contain fewer T cells, CD8aa included, in draws 1, 2 and 4 (direction-level gates). This mostly reflects the bulk's lymphopenia signal. No draw supports a CD8αα-specific depletion. Strength should drop from weak to bulk-derivable / not CD8αα-specific.\n\nI used CPU only and started no GPU work. All scripts and tables are in my lens directory. I published no archive delta; whether to publish one is the coordinator's call.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-4_recompute/v05_observed.py",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-4_recompute/v11_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-4_recompute/v11_summary.txt"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a5b367e6ddc2c4436",
+     "verdict": "refuted",
+     "recomputed": "Claim numbers reproduce (v04). Bulk-only CD8aa share of all cells, peak-severity logit by draw 0-4: -0.03 (p .8), -0.38 (1e-4), -0.59 (2e-4), -0.34 (.04), -0.41 (7e-5). Wave 1: -0.05, -0.34, -0.52, -0.34, -0.38. Observed paired cells: -0.66 (p .003). Donor-level check: -0.68 (p .002), 14 severe vs 21 mild donors. Observed wave 1: -0.77 (p 7e-4). My recomputed COVID+ gate for this row: DIR DIR PASS DIR DIR (gen/obs coefficient ratio 0.37 0.81 0.70 0.25 0.42).\n\nDecomposition, observed. The observed T-cell share does not change with peak severity: -0.06 (p .88); wave 1 -0.08 (p .79). The observed effect lies inside the T compartment: CD8aa as a share of T cells -0.73 (p 3e-4; donor-level -0.74, p 8e-5). This barely changes after adjusting for the T share (-0.63), an immediate-early gene program (-0.77) or a bulk T-cell score (-0.73).\n\nDecomposition, bulk-only generated. The T share falls in every draw: -0.49, -0.51, -1.07, -0.99, -0.63 (all p <= 1e-3). CD8aa as a share of T cells does not fall in any draw: +0.20 (p .05), -0.16 (.09), +0.03, +0.24 (.04), -0.08. Wave 1: +0.21, -0.16, +0.13, +0.26, +0.00.\n\nT-subset controls (v05). In bulk-only, 3-5 of 10 T subsets fall significantly per draw, and CD8aa ranks only 8, 4, 5, 5, 3 among them (1 = largest fall). Memory CD8 falls -0.10/-0.52/-0.90/-1.01/-0.60 and gdT -0.23/-0.38/-0.58/-0.73/-0.42; in observed cells these are +0.22 and +0.16. In observed cells, CD8aa is the only T subset that falls.\n\nPaired calibration of CD8aa as a share of T cells (this feature is not in calibration.csv; computed here):\n- Mixed model: gen -0.16, -0.44, -0.21, +0.30, -0.20 vs obs about -0.70. Gates DIR FAIL DIR FAIL DIR; coefficient ratio 0.23 0.61 0.30 -0.36 0.28; level 0.56 0.32 0.62 0.38 0.60.\n- Official format (raw means): gen/obs difference ratio 0.22, 0.18, 0.30, opposite sign, 0.23; gates DIR FAIL DIR FAIL DIR.\n\nOfficial calibration.csv T-cell severity row: same sign in only 2/5 draws (gen -0.015 -0.004 -0.076 -0.128 +0.020 vs obs +0.004 +0.006 -0.027 +0.008 +0.013).\n\nAdjustments in bulk-only:\n- For a canonical bulk T-cell score: -0.38 to -0.24, -0.59 to -0.23, -0.34 to +0.02, -0.41 to -0.21. That removes 37%, 61% and 49% of the effect in draws 1, 2 and 4, and all of it in draw 3.\n- For each draw's own generated T share: +0.02, -0.32 (p .001), -0.19 (.09), +0.18, -0.21 (.05).\n\nTransfer. The bulk granulocyte (LDG) score in severe/critical donors is +0.70 in bulk-only vs +0.31 in paired (Mann-Whitney p .009). The bulk T-score severity effect is -0.92 SD in bulk-only vs -0.43 SD in paired.",
+     "issues": [
+      "Not CD8aa-specific. In bulk-only generated cells, the fall in CD8aa as a share of all cells comes from a T-cell-wide fall: the T share drops -0.49 to -1.07 logit in all 5 draws. CD8aa as a share of T cells falls in no draw (+0.20, -0.16, +0.03, +0.24, -0.08); draws 0 and 3 move the opposite way at p of about .04-.05. Memory CD8 and gdT fall as much as or more than CD8aa, and CD8aa ranks only 3rd to 8th of 10 T subsets (v05).",
+      "Observed cells contradict the generated mechanism. In observed cells the T share does not change with peak severity (-0.06, p .88; wave 1 -0.08, p .79), CD8aa is the only T subset that falls, and the effect lies inside the T compartment (-0.73, p 3e-4). 'Observed cells show the same drop' is true only on the all-cell scale; it is a different drop.",
+      "The T-share part fails the official trust layer. The calibration.csv T-cell severity row keeps the sign in only 2/5 draws. In paired wave-1 test samples the generator makes a T-share fall (-0.37, -0.02, -0.90, -1.08, -0.47) that observed cells do not show.",
+      "Within-T calibration, computed here because calibration.csv lacks the feature: gates DIR/FAIL/DIR/FAIL/DIR. The generator reproduces at most 30% of the observed within-T contrast in draws 0, 2 and 4, and the opposite sign in draw 3. Draw 1 reproduces 61% of the logit effect but makes CD8aa about 3 times too rare among T cells (level 0.31-0.32, below the 1/3 gate).",
+      "Only draw 1 carries a CD8aa-specific bulk-only signal, and only weakly. Within T: -0.16 (p .09), or -0.18 (p .05) without its training donors' samples. After adjusting for the draw's own T share: -0.32 (p .001). Draws 2 and 4, which supply the headline -0.59 and -0.41, give +0.03 and -0.08 within T.",
+      "The calibration may not transfer. Bulk-only severe/critical donors carry more granulocyte (LDG) RNA in their bulk than paired severe donors (+0.70 vs +0.31 SD, Mann-Whitney p .009) and were sampled later (13.5 vs 9.4 days, p .04). The bulk LDG score anticorrelates with the generated T share (rho -0.35 to -0.48). Gisby 2022 (PDF p. 18): bulk RNA came from fresh PBMC, and LDGs do not survive the freeze-thaw of the cryopreserved cells that were sequenced as single cells.",
+      "Little beyond the bulk. A canonical bulk T-cell score removes 37%, 61% and 49% of the bulk-only effect in draws 1, 2 and 4 and all of it in draw 3 (residuals -0.24, -0.23, -0.21, +0.02). The same adjustment barely moves the observed effect (-0.66 to -0.54).",
+      "Unit mix-up in the claim. The 'LDG adjustment -0.65, -0.66, -0.74' values in s12_ldg.csv are in SD units; the unadjusted values on that scale are -0.74, -0.78, -0.86. So LDG adjustment shrinks the effect by 12-18%, and these numbers cannot be compared with the logit effects quoted beside them.",
+      "The official calibration.csv row 'severe/critical vs mild/moderate (COVID+)' puts the 9-10 convalescent 'recovery' samples in the severe arm (draw 0: 49 vs 62, where 49 = 40 severe COVID+ samples + 9 recovery samples). Restricted to COVID+ samples, draw 0's CD8aa gate is DIR, not PASS.",
+      "The label's biology does not fit 'innate-like CD8 alpha-alpha'. In this cohort's observed cells, CD8aa-labelled cells express CD8B: 75% of CD8A+ cells are CD8B+, the same as memory CD8 at matched depth, and pseudobulk CD8B is 1,189 CPM vs 990 for CD8A. They show few innate-like markers: KLRB1 5%, SLC4A10 0%, ZBTB16 3% (MAIT cells: 77%, 25%, 11%). They are IL7R 86%, GZMK 45%, CCR7 16%, which looks like conventional CD8 alpha-beta memory (central-memory-like) cells. The AIFI label comes from a paediatric CD8 alpha-alpha subset (Gong 2025 PDF p. 11, ref. 32; paediatric reference samples p. 13), whose protein-defined identity cannot be checked here.",
+      "Novelty. Stephenson 2025 (PDF p. 3) already reports lower MAIT, CD4 and CD8 central-memory and CD4 effector-memory proportions in COVID+ vs COVID-. Its composition analysis by severity at sampling (PDF p. 6) found only antibody-secreting B cells changed. With that same contrast, our observed CD8aa effect is weak too (-0.25, p .10). Severity-linked MAIT and CD8 effector-memory loss is established in other cohorts (Stephenson 2021 PDF p. 5; COMBAT 2022 PDF pp. 8 and 12, which also notes minimal compositional variation with severity on p. 10). The peak-severity within-T decline is unreported for this cohort, but it is an observed single-cell result in 35 donors, not a bulk-only discovery.",
+      "The MAIT companion feature has the same flaw. In draws 2-4 the bulk-only MAIT fall is driven by the T share: MAIT as a share of T cells is -0.27, -0.17, -0.01 (wave 1: -0.13, -0.26, -0.06). Only draw 1 falls within T (-0.66, p .001). The official MAIT severity gate is FAIL/DIR/DIR/DIR/FAIL (Spearman 0.15-0.39), and the observed all-wave MAIT effect has p .088.",
+      "Not contested: the observed association is real and not a handling artifact. It holds at donor level (Mann-Whitney AUC 0.21, p .005), in both sexes (males -0.53, females -1.02), against an immediate-early program (rho 0.00 with CD8aa as a share of T; adjusted -0.77) and against the bulk T-cell score. Shared seed noise (10.5%) makes draw agreement non-independent, but the per-draw within-T test already fails in 4/5 draws."
+     ],
+     "reasoning": "Steps 1-2: I re-implemented the claim's model independently (REML mixed model on logit fractions with a donor random intercept, plus donor-level checks). All of the claim's numbers reproduce: bulk-only -0.38, -0.59, -0.41 in draws 1, 2, 4; observed -0.66 (p .003); recomputed gates DIR DIR PASS DIR DIR on the CD8aa share of all cells. Taken alone, that row supports the claim.\n\nThe adversarial step was to split CD8aa's share of all cells into the T-cell share and CD8aa's share within T cells.\n- Observed cells: the T share does not change with severity, and CD8aa is the only T subset that falls. The effect lies entirely inside the T compartment (-0.73, p 3e-4) and survives every adjustment.\n- Generated bulk-only cells: the opposite. The T share collapses with severity in every draw, and nearly all T subsets fall with it, including memory CD8 and gdT, which are flat in observed cells. CD8aa within T does not fall in any draw; two draws go the other way.\n- The T-share part fails the official trust layer: the T-cell severity sign matches in only 2/5 draws.\n- The within-T paired calibration fails or is weak (at most 30% of the observed effect) in 4/5 draws.\n- A plain bulk T-cell score explains 37-100% of the generated effect.\n- Bulk-only severe donors carry more granulocyte (LDG) RNA than the paired calibration donors. That RNA exists in fresh-PBMC bulk but not in the cryopreserved single cells, and the generator turns it into fewer T cells.\n\nSo the generated cells reproduce the observed all-cell direction for the wrong reason. The headline draws 2 and 4 show nothing within T. Only draw 1 shows a weak CD8aa-specific trend, and its within-T calibration sits just below the level gate. Because observed cells and the trust layer contradict the mechanism of the generated effect in the draws that carry it, I rule it refuted rather than qualified.\n\nStep 3, biology and novelty: in this cohort the 'CD8aa' label marks CD8B-expressing, IL7R+ GZMK+ memory-like CD8 cells, not innate-like CD8 alpha-alpha cells. The cohort papers do not report a severity effect for this population: Stephenson 2025 PDF p. 6 found only antibody-secreting B cells changed with severity at sampling, and PDF p. 3 reports the COVID+ vs COVID- decline of MAIT and CD8 central-memory cells. Loss of MAIT and CD8 effector-memory cells with severity is established in other cohorts (Stephenson 2021 p. 5; COMBAT 2022 pp. 8, 12).\n\nStep 4, beyond bulk: little remains after the bulk T-cell score.\n\nWhat survives: the observed single-cell association (CD8aa as a share of T cells falls with peak severity, 35 donors). It is robust but is an observed-data result, not a bulk-only finding.\n\nHousekeeping: CPU only, no GPU work, and writes stayed in my lens directory. There is no archive change from this verifier; the coordinator owns the task-end paper-archive step.",
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+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-4_calib/v10_key_numbers.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-CLIN-4_calib/v10_key_numbers.csv"
+     ]
+    }
+   ]
+  },
+  {
+   "id": "IMP-LONG-2",
+   "lens": "imp_longitudinal",
+   "final": "refuted",
+   "verdict_recompute": "refuted",
+   "verdict_calib": "refuted",
+   "synthesis": {
+    "rank": 21,
+    "theme": "refuted (label artefact)",
+    "synthesis_strength": "refuted: annotation artefact",
+    "reportable_statement": "Not reportable. The frozen labeller reads the acute ISG programme as 'naive B': adding acute-phase ISG counts to late low-ISG B cells flips 41-64% of memory labels to naive, and 45% of isotype-validated IgG/IgA switched memory cells in the top ISG quartile are called naive. With the cohort authors' CITE-seq/BCR labels the wave-1 memory share does not rise (+0.015/day, p 0.15), and the generated wave-1 slope is 2.8-4.2x that truth in every draw. What survives is that AIFI L2 memory/naive B labels track the bulk IFN clock.",
+    "carrying_draws": {
+     "label-level slope": "0-4",
+     "after ISG-neutral relabelling": "3 only (weak within donors)",
+     "against author labels": "none"
+    },
+    "honesty": {
+     "calibration": "Label-level TIME_PASS 5/5, but generated and observed share the same biased labeller; against author labels the wave-1 slope ratio is 2.8-4.2 (FAIL 5/5).",
+     "replication": "Label-level 5/5; ISG-neutral relabelling removes the slope in draws 0, 1, 2, 4.",
+     "observed": "Author-label memory share in wave 1: 0.38 -> 0.35 -> 0.33 (COVID- 0.42); a real rise only in wave 2 (+0.075/day, 15/15 donors).",
+     "beyond_bulk": "None: bulk ISG removes the slope; bulk ISG tracks within-donor change in the author truth better than the generated share."
+    },
+    "verdict_reasons": [
+     "Both verifiers refuted: label artefact demonstrated by counterfactual relabelling and by the authors' protein/BCR labels."
+    ],
+    "refuted_subclaims": [
+     "memory-share trajectory",
+     "within-person inverse coupling to IFN as new biology",
+     "stays high after day 21"
+    ],
+    "synthesis_note": "Implication for IMP-CLIN-1: the week-1 'naive B' enrichment is partly this label artefact.",
+    "new_vs_cohort_papers": "Stephenson 2025 Fig. 4A (IFN-stimulated switched memory B raised in weeks 1-2) - exactly the cells mislabelled naive.",
+    "next_steps": [
+     "Use the authors' CITE-seq/BCR B labels or an ISG-robust reference for every B-subset contrast."
+    ],
+    "key_sources": [
+     "verify/IMP-LONG-2_recompute/v08_per_draw_summary.csv",
+     "verify/IMP-LONG-2_recompute/v05_flips_obs_bo.csv",
+     "verify/IMP-LONG-2_recompute/v06_flip_validation.csv",
+     "verify/IMP-LONG-2_recompute/v03_reference_isg.csv",
+     "verify/IMP-LONG-2_calib/v03_crosstab.csv",
+     "verify/IMP-LONG-2_calib/v03_truth_slopes.csv",
+     "verify/IMP-LONG-2_calib/v03_gen_vs_author.csv",
+     "verify/IMP-LONG-2_calib/v04_rule_slopes.csv"
+    ]
+   },
+   "finding": {
+    "id": "IMP-LONG-2",
+    "cohort": "imperial_eskd",
+    "claim": "In bulk-only donors, the generated memory share of B cells (memory / (memory + naive), AIFI L2) rises from week 1 to weeks 2-3 and stays high after day 21: +0.046 to +0.075 logit/day in every draw, including donors without single cells. This matches the observed paired rise in donors from the same wave. The bulk-only donors' own memory-vs-naive B marker score shows no rise, but the falling bulk ISG score predicts the rise fully, in both observed and generated cells.",
+    "kind": "trajectory",
+    "feature": "memB_share = l2 | Memory B cell / (l2 | Memory B cell + l2 | Naive B cell). Derived feature.",
+    "comparison": "Days since first positive swab (0-21 d, plus >21 d in bulk-only). COVID+ samples. Bulk-only generated cells per draw vs observed paired cells, all waves and wave-matched (wave 1).",
+    "effect_bulkonly": "Raw window means, d0-7/d8-14/d15-21/>21:\n- d0: 0.43/0.57/0.57/0.58\n- d1: 0.42/0.57/0.58/0.63\n- d2: 0.44/0.60/0.60/0.63\n- d3: 0.26/0.40/0.41/0.44\n- d4: 0.41/0.57/0.57/0.58\n\nSlopes (logit/day):\n- Severity-adjusted, <=21 d (127 samples / 57 donors): +0.046, +0.073, +0.064, +0.075, +0.059.\n- Wave 1 (110 / 47): +0.048, +0.081, +0.067, +0.081, +0.061.\n- No-single-cell donors (77 / 31): +0.055, +0.090, +0.083, +0.092, +0.072.\n- Excluding training donors and the sex-flag sample: +0.053 to +0.085.\n\nSeverity-adjusted window effects vs d0-7: d15-21 is +0.53 to +0.96 logit; >21 d is +0.77 to +1.30.",
+    "stats": "Model: MixedLM (ML), logit(memB_share) ~ days + severity_peak + wave + (1|donor).\n- Per-draw p: 2e-4 to 4e-9.\n- Wave-1 BH q (51 features per draw): 6e-4 to 2e-9.\n- Within-donor FE slope: +0.038 (p 0.08, d0), +0.052 to +0.076 (p <= 0.008, d1-d4).\n- Per-donor slopes, Wilcoxon: p <= 0.003 in d1-d4, 0.10 in d0; 63-77% of donors positive.\n\nThe feature was defined post hoc, after s02/s04 showed memory B rising and naive B falling. Its components are in the BH-corrected 50-feature screen: memory B q <= 0.009 in draws 0,1,2,4; naive B q <= 0.015 in draws 3,4.",
+    "draws_same_sign": "5/5. Shared seed and the same bulk input, so not independent.",
+    "paired_calibration": "Not in calibration.csv as a derived feature. Its components there:\n- Memory B: ratio 0.80-2.32, Spearman 0.44-0.60; COVID+/- sign 3/5, severity sign 2/5.\n- Naive B: ratio 0.45-0.86, Spearman 0.54-0.62; severity sign 0/5.\n\nRecomputed for memB_share on COVID+ paired test samples (s10):\n- Ratio 1.01-1.53, Spearman 0.43-0.68.\n- Within-donor tracking r 0.75-0.84; slope ratio 0.69-0.87.\n- TIME_PASS 5/5.\n\nWave 1 (s16): ratio 0.99-1.42, Spearman 0.31-0.63, tracking 0.58-0.75, slope ratio 0.92-1.27. W1_TIME_PASS 5/5.",
+    "paired_observed_concordance": "Yes, when the waves are matched.\n- Observed paired cells: 0.27 -> 0.39 -> 0.52. Slope +0.111/day (p 3e-13); 84% of donors positive; FE slope +0.114.\n- By wave: +0.053 in wave 1 (p 0.002), +0.166 in wave 2 (wave x day p 4e-5).\n- Bulk-only wave-1 slopes are 0.9-1.5x the observed wave-1 slope. Paired-test generated wave-1 slopes: +0.054 to +0.085.\n- In observed cells the share rises faster in severe donors (severity x day +0.10, p 4e-4), but only in wave 2 (wave 1: +0.008, p 0.84). Bulk-only wave 1 likewise shows no severity difference (-0.023 to -0.044).",
+    "beyond_bulk": "It goes beyond a bulk B-subset marker score, but not beyond the bulk IFN clock.\n- Bulk memory-minus-naive marker score: +0.040 SD/day in paired samples (p 1e-5), but -0.004 in bulk-only (p 0.76). In wave-1 paired samples it is also flat (+0.012, p 0.35) while the observed cells rise.\n- The generated slope is unchanged after adjusting for that score (+0.044 to +0.075).\n- The bulk ISG score correlates with the observed share within donors at r = -0.79. Adjusting for it removes the slope in observed cells (+0.003, p 0.89; wave 1 -0.027, p 0.18) and in generated cells (+0.000 to +0.016).\n- After removing ISG, a B-specific residual remains: partial within-donor tracking r 0.23-0.56.\n- For between-sample levels, the bulk B-subset score predicts the observed share better than the generated cells (Spearman 0.73-0.77 vs 0.43-0.68).",
+    "novelty": "Partly reported.\n- Stephenson 2025, PDF p.3 (journal p.2): naive B proportion is higher in week 1 than in COVID- samples.\n- Stephenson 2025, PDF p.9: IFN-stimulated switched memory B cells stay raised into weeks 2-3.\n- Not reported in either paper (text search): the within-B memory-share trajectory, and its within-person inverse coupling to the IFN response.\n- Gisby 2022 reports no B-subset trajectory, only the plasma-cell module tL (PDF p.5).",
+    "strength": "moderate",
+    "files": [
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+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s11_isg_adjusted.csv",
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+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s13_within_donor.csv",
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+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s19_sevxday_by_wave.csv",
+     "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/s21_findings_numbers.txt"
+    ]
+   },
+   "verdicts": [
+    {
+     "verifier": "recompute",
+     "agent_id": "a211d63044df1aee9",
+     "verdict": "refuted",
+     "recomputed": "All numbers below come from my own code, run on the raw cells.parquet, pb_l2, subsample and markers files and the bulk npz files.\n\n1. Label-level reproduction (full cells; v01, v02)\n- My recount of Memory B and Naive B matches composition.parquet exactly (max difference 0).\n- Severity- and wave-adjusted MixedLM slope, COVID+ bulk-only samples ≤21 d (127 samples, 57 donors), per draw d0–d4: +0.046, +0.073, +0.064, +0.075, +0.059 logit/day (p 1e-4 or smaller).\n- Wave 1: +0.048, +0.081, +0.067, +0.081, +0.061.\n- Donors without single cells (77 samples, 31 donors, all wave 1): +0.055, +0.090, +0.083, +0.092, +0.072.\n- Without training-donor and sex_flag samples: +0.053, +0.080, +0.064, +0.085, +0.062.\n- Within-donor FE: +0.038 (p 0.08), +0.076, +0.067, +0.071, +0.052 (p ≤ 0.008).\n- Per-donor slopes: 63–77% positive; Wilcoxon p 0.10, 2e-4, 0.002, 5e-4, 0.003.\n- Window effects vs d0–7: d15–21 +0.53/+0.90/+0.79/+0.93/+0.76; >21 d +0.77/+1.30/+1.12/+1.29/+0.89.\n- The slope survives each of these: age, sex, ethnicity, diabetes, fatal outcome, WHO severity at the sample, number of B cells, within-label B library size, raw bulk library, generation part/seed, symptom-based clocks, random slopes, donor-clustered OLS, cell-level GEE (+0.034 to +0.070), and confidence ≥0.9 shares (+0.058 to +0.106).\n- All 16 samples after day 21 (10 donors) are severe/critical and wave 1. Paired truth stops at day 20.\n\n2. Observed truth (paired cells, label level)\n- +0.110/day (p 1e-12).\n- Wave 1: +0.053 (p 0.002).\n- Wave 2: +0.166.\n\n3. Trust layer, derived feature (COVID+ paired test, full cells)\n- All waves: ratio 1.50/1.48/1.53/1.01/1.41; Spearman 0.60/0.60/0.64/0.43/0.68; within-donor r 0.79/0.80/0.76/0.75/0.84; slope ratio 0.69/0.87/0.78/0.81/0.76.\n- Wave 1: ratio 0.99–1.42; Spearman 0.31–0.63; within-donor r 0.58–0.75; slope ratio 0.92–1.27.\n- Contrasts: COVID+ vs COVID− sign 5/5; severe vs mild sign 4/5 (d0 wrong).\n- calibration.csv components: Memory B ratio 0.80–2.32, Spearman 0.44–0.60, COVID± sign 3/5, severity sign 2/5. Naive B ratio 0.45–0.86, Spearman 0.54–0.62, COVID± sign 5/5, severity sign 0/5.\n\n4. Bulk (v07)\n- Adjusting for the bulk ISG z-score removes the slope everywhere: observed −0.005, observed wave 1 −0.027, paired-test generated −0.004 to +0.021, bulk-only −0.001 to +0.007.\n- Bulk memory-minus-naive marker score:\n  - paired wave 1: −0.002 SD/day (p 0.88);\n  - bulk-only: −0.030 SD/day (p 0.014); wave 1 −0.037 (p 0.002). It falls rather than rises.\n- Paired and bulk-only bulk profiles match within wave-1 windows: p ≥ 0.13 for ISG, B-subset score, B content and library size.\n\n5. The ISG label effect (new tests)\n- The frozen f01common reference has an 'ISG+ naive B cell' L3 class. It holds 803 cells, with mean ISG 5.29 against 2.4–3.6 for every other B type, and 90% of them are in the top 10% of ISG. There is no ISG-high memory class.\n- ISG swap through the frozen scANVI (CPU, 38 core ISGs, counts rescaled to the cell's library and Poisson-sampled):\n  - Giving low-ISG late B cells the ISG counts of acute ISG-high B cells flips Memory to Naive in 64% of observed cells and 41–58% in the five bulk-only draws. A matched non-ISG gene swap flips 1–14%.\n  - With donors that the classifier itself called Memory, the flip rate is 31% at top-quartile ISG and ≤2.5% at the lower quartiles.\n- Removing ISG from all B cells (de-ISG relabelling):\n  - Week-1 share rises: observed 0.17→0.27; bulk-only 0.44→0.59, 0.42→0.56, 0.45→0.61, 0.21→0.33, 0.37→0.51.\n  - The late and COVID− share barely moves.\n  - The cells that flip have a non-ISG memory-minus-naive marker score of −0.21 in observed cells, against −1.30 for cells that stay naive and +0.08 for late memory cells (Mann–Whitney p < 1e-4 in all six sets).\n- Slopes on the same subsample cells, original → de-ISG labels:\n  - Observed: 0.100 → 0.050 (p 0.003). Observed wave 1: 0.060 → 0.019 (p 0.45).\n  - Bulk-only d0–d4: 0.034→−0.021 (p 0.15); 0.050→0.012 (p 0.39); 0.053→−0.000 (p 0.98); 0.071→0.047 (p 0.004); 0.056→0.015 (p 0.19).\n  - The control swap keeps 0.039–0.103.\n  - Using only Naive-labelled donor cells gives the same result: −0.022, 0.012, 0.009, 0.047, 0.016; observed wave 1 0.017.\n- Label-free per-cell marker share:\n  - Observed wave 1: 0.010 (p 0.64).\n  - Bulk-only: −0.016, −0.005, +0.008, +0.039 (p 0.019), −0.003. Within-donor FE −0.021 to +0.010 in all five draws.\n- Pooled-B pseudobulk marker score (full cells):\n  - Observed wave 1: +0.017 (p 0.44), which is 32% of what the label change implies.\n  - Bulk-only, as a share of the label-implied change: 1%, 79%, 103%, 99%, 42%. Within-donor FE −0.016, 0.059 (p 0.05), 0.023, 0.060 (p 0.15), −0.002.\n- Paired-test generated cells, original → de-ISG slope: 0.083→0.038, 0.080→0.047, 0.079→0.059, 0.104→0.059, 0.092→0.047.\n- Calibration on the same subsample cells, original → de-ISG: within-donor r 0.51–0.61 → 0.11–0.40; wave-1 Spearman 0.17–0.51 → −0.04–0.27.",
+     "issues": [
+      "Annotation artifact. The frozen f01common scANVI reads the acute ISG program as 'Naive B'. Its reference has an 'ISG+ naive B cell' class and no ISG-high memory class. Adding acute-phase ISG counts to low-ISG late B cells flips 41-64% of Memory labels to Naive; a matched non-ISG swap flips 1-14%. The 'memory share' therefore moves with ISG, not necessarily with B-cell composition.",
+      "The effect disappears under an ISG-neutral control in 4 of 5 draws. De-ISG relabelling slopes: d0 -0.021, d1 +0.012, d2 -0.000, d4 +0.015 (all p >= 0.15). The label-free per-cell marker share is also flat in d0, d1, d2 and d4, and its within-donor slope is about 0 in all 5 draws. Only draw 3 keeps a slope (de-ISG +0.047, p 0.004; marker +0.039, p 0.019), with a within-donor marker FE of only +0.010 (p 0.68).",
+      "The claimed match with the wave-1 observed rise compares two ISG label artifacts. In observed wave-1 cells: de-ISG slope 0.019 (p 0.45), label-free marker share 0.010 (p 0.64), pooled-B marker score 0.017 (p 0.44), bulk memory-minus-naive score -0.002 (p 0.88). A real component exists only in wave 2 (observed de-ISG all-wave slope 0.050), and bulk-only has just 17 wave-2 samples.",
+      "The trust layer passes only at the label level. TIME_PASS 5/5 reproduces exactly, but generated and observed cells are labelled by the same classifier, so a shared ISG-to-Naive artifact passes it. After ISG neutralisation, within-donor tracking drops from 0.51-0.61 to 0.11-0.40 and wave-1 Spearman to -0.04 to 0.27. Component rows in calibration.csv: Memory B COVID+/- sign 3/5 (d1, d3 wrong) and severity sign 2/5; Naive B severity sign 0/5.",
+      "Nothing beyond the bulk. Adjusting for the bulk ISG z-score removes the slope in every draw (+0.000 to +0.007) and in observed cells (-0.005). The bulk-only memory-minus-naive bulk score falls with time (-0.030 SD/day, p 0.014; wave 1 -0.037, p 0.002).",
+      "'Stays high after day 21' rests on 16 samples from 10 donors, all severe/critical and all wave 1. Paired truth stops at day 20, so this part is uncalibrated. Under de-ISG labels the >21 d window looks like days 8-21; nothing specific to after day 21 remains.",
+      "The novelty claim ('within-person inverse coupling to the IFN response') is explained by the classifier's ISG-to-Naive mapping. Stephenson 2025 reports IFN-stimulated switched memory B cells in weeks 2-3, which is exactly the kind of cell this classifier would call Naive in week 1.",
+      "Label confidence. Memory-label confidence rises with days (bulk-only 0.86 to 0.91; observed 0.86 to 0.96). Adjusting for it removes the d0 slope (0.016, p 0.16). Filtering at confidence >= 0.9 does not remove the slope, because 'ISG+ naive' calls are themselves confident.",
+      "The headline label-level numbers reproduce exactly, and the slope survives age, sex, ethnicity, diabetes, fatal outcome, WHO severity at the sample, wave-1 restriction, sex_flag and training-donor exclusion, donor fixed effects, random slopes, B-cell number, within-label library size, bulk raw library, generation part and alternative clocks. The paired and bulk-only bulk profiles match within wave-1 windows.",
+      "All draws used seed 20260927, so the 5/5 draw agreement is not five independent replications.",
+      "Limits of my tests. The de-ISG and marker-share analyses use the 200-cell subsamples (noisier; the original-label slopes there are about 70% of the full-cell values). De-ISG swaps only 38 core ISGs, so it is conservative. The cell-level GEE on observed cells did not converge (observed GEE row in v02 is invalid); the bulk-only GEE rows are valid."
+     ],
+     "reasoning": "The label-level numbers reproduce exactly, and the usual confounders do not move them: age, sex, ethnicity, diabetes, fatal outcome, WHO severity at the sample, wave, sex_flag, training-donor samples, repeated visits (fixed effects, per-donor slopes, random slopes), cell number, library size, generation part and alternative clocks.\n\nThe decisive confounder is the measurement itself. AIFI L2 memory and naive labels come from a frozen scANVI whose reference has an 'ISG+ naive B cell' class and no ISG-high memory class. I ran a direct in-silico test through that classifier on CPU (CUDA_VISIBLE_DEVICES='', --device cpu, the same CPU path used to label these files; no GPU work).\n- Adding acute ISG counts to low-ISG late B cells flips about half of the Memory labels to Naive. A matched non-ISG swap does almost nothing.\n- Removing ISG raises the week-1 memory share. The cells that flip are memory-like on non-ISG markers (CD27, TNFRSF13B, SCIMP, COCH, SSPN, CD80 against TCL1A, FCER2, IL4R, BACH2, PLPP5, YBX3, CD200).\n- On identical cells, ISG-neutral labels remove the bulk-only rise in draws 0, 1, 2 and 4. They also remove the wave-1 observed rise that the claim relies on for concordance.\n- The result does not change when donor ISG counts come only from Naive-labelled cells, so it is not imported memory identity.\n- A label-free per-cell marker share, a pooled-B pseudobulk score and the bulk B-subset score all agree: no wave-1 composition rise in observed cells, and no within-donor rise in bulk-only cells.\n\nSo the claim's own observation that bulk ISG predicts the rise fully is real, but it is explained by how the classifier maps ISG, not by B-cell biology.\n\nWhich draws carry a result:\n- At label level, all 5 draws pass their own derived-feature calibration (TIME_PASS 5/5, wave-1 5/5).\n- After ISG-neutral control, only draw 3 keeps a slope, and its within-donor support is weak.\n- Under the guide's honesty criteria:\n  - calibration is label-level only and cannot detect this shared artifact;\n  - replication is 1 of 5 draws after the control (shared seed);\n  - observed agreement in wave 1 is absent once ISG is neutralised;\n  - nothing goes beyond the bulk ISG clock.\n\nThe '>21 d' part has no paired truth and contains only severe wave-1 donors.\n\nVerdict: refuted as a finding about the B-cell memory share. What survives is 'generated AIFI-L2 labels track the bulk ISG clock', which can be read straight off the bulk and is an annotation artifact.\n\nAll scripts and tables are only in the verify directory, so there is no paper-archive change from this verification.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v08_per_draw_summary.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v08_summary.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v08_summary.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v08_windows_deisg.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v08_libcheck.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v01_build.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v01_samples.parquet",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v02_trajectories.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v02_traj.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v02_trajectories.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v02_confounder_vs_days.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v03_reference_isg.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v03_reference_isg.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v04_labelfree.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v04_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v05_isg_swap.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v05_isg_swap.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v05_flips_obs_bo.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v05_traj_obs_bo.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_isg_swap_more.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_isg_swap_more.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_flip_validation.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_isgin_memdonor.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_traj_pt.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v06_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_bulk_markers_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_bulk_markers_calibration.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_bulk_adjusted.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_marker_share_traj.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_calibration.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_calibration_csv_rows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v07_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v09_deisg_naive_donors.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v09_traj.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/v09_flips.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/vlib.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_recompute/run.sh"
+     ]
+    },
+    {
+     "verifier": "calib",
+     "agent_id": "a87533ddb9bc4a159",
+     "verdict": "refuted",
+     "recomputed": "REPRODUCED (AIFI L2 labels on both sides; scripts v01 and v05). The bulk-only slopes match the candidate's.\n- Severity-adjusted, <=21 d: +0.046, +0.074, +0.064, +0.075, +0.059.\n- Wave 1: +0.048, +0.081, +0.067, +0.081, +0.061.\n- No-single-cell donors: +0.055, +0.090, +0.083, +0.092, +0.073.\n- Sensitivity set: +0.053 to +0.085.\n- Within-donor (fixed-effect) slope in draw 0: +0.038 (p 0.079); wave 1 +0.033 (p 0.17).\n\nPaired-test calibration, generated AIFI share vs observed AIFI share:\n- COVID+ <=21 d: ratio 1.01-1.53, Spearman 0.43-0.68, within-donor tracking 0.75-0.84, slope ratio 0.69-0.87.\n- Wave 1: ratio 0.99-1.42, Spearman 0.31-0.63, tracking 0.58-0.75, slope ratio 0.92-1.27. Bootstrap CIs of the within-donor slope ratio span about 0.54-3.5. Visit-to-visit sign agreement is 0.56-0.71 and passes p<0.05 in 1/5 draws.\n- COVID+ vs COVID-: same sign in 5/5 draws. Severe vs mild: 4/5. Severe vs mild in wave 1, <=21 d: 3/5.\n\nNEW: gold-standard truth from the cohort authors' own labels (CITE-seq CD27/CD11c plus BCR isotype; cells_obs.parquet, matched cell by cell).\n- Isotype-validated IgG/IgA switched memory B cells in COVID+ samples, by the cell's ISG quartile:\n  - AIFI calls them naive: 3.3%, 1.7%, 8.9%, 44.9%.\n  - AIFI calls them memory: 83%, 79%, 70%, 37%.\n- Share of author-memory cells that AIFI calls naive, per sample, wave 1: 18.4% -> 7.6% -> 1.3% across d0-7 / d8-14 / d15-21. Higher bulk ISG raises it (+1.09 logit per SD, p<1e-4).\n- Author memory share, COVID+ <=21 d, wave 1:\n  - Slope +0.015/day (95% CI -0.005 to +0.036, p 0.15). Within-donor slope +0.017 (p 0.31).\n  - Per-donor median +0.018; 69% of donors positive; Wilcoxon p 0.14.\n  - Window means 0.38 / 0.35 / 0.33; COVID- is 0.42.\n  - The AIFI share on the same samples rises +0.053 (p 0.002).\n- An ISG-robust RNA marker rule agrees with the author labels equally well in every ISG quartile (calls 81-87% of author-memory cells memory; calls 3-5% of author-naive cells memory). Observed wave-1 slope: -0.005 (p 0.83).\n- Wave 2 does rise in the author labels: +0.075/day; 15/15 donors positive. All waves: +0.046 author vs +0.110 AIFI.\n\nGenerated vs author truth, paired test samples:\n- Wave 1: slope ratio 2.8-4.2 (fails in 5/5 draws); level Spearman 0.11-0.48.\n- Slope of (generated - author) on the same samples: +0.038 to +0.062/day, p 0.0002-0.011 in 5/5 draws. Slope of (generated - AIFI): about 0 (p 0.39-0.85).\n- Bulk-only wave-1 slopes (+0.048 to +0.081) all lie above the truth's upper 95% bound (+0.036).\n- ISG coupling, logit per SD, wave 1: author -0.30, AIFI -0.68, generated -0.46 to -0.85.\n- Same generated cells, two labels, bulk-only wave 1:\n  - AIFI share slope: +0.037 to +0.080.\n  - Marker-rule slope: -0.003 to +0.039; not significant in draws 0-2.\n  - Generated marker-rule memory cells that AIFI calls naive: 26-40% at d0-7 and 37-61% in the top ISG quartile.\n\nDays over 21: 16 bulk-only samples from 10 donors, all wave 1 and all severe/critical. No paired COVID+ sample is later than day 20.\n\nBulk alone vs author truth, wave 1, each draw's own test samples:\n- The bulk ISG score tracks within-donor change at r 0.61-0.68; the generated share at 0.33-0.60.\n- A bulk B-subset proxy ranks sample levels at Spearman 0.52-0.60; the generated share at 0.12-0.48.\n\nTransfer check: bulk ISG decays at the same rate in paired and bulk-only wave-1 samples (-0.095 vs -0.100 SD/day).",
+     "issues": [
+      "The trajectory comes from the label. AIFI L2 is RNA-only and calls ISG-high memory B cells naive: 45% of isotype-validated IgG/IgA switched memory cells in the top ISG quartile, vs 2-3% at low ISG. As IFN falls over time, the AIFI memory share rises even when composition does not change.",
+      "The observed truth contradicts the wave-matched claim. With the cohort authors' CITE-seq/BCR labels, the wave-1 memory share does not rise: +0.015/day (CI -0.005 to +0.036, p 0.15); window means 0.38/0.35/0.33. The observed wave-1 AIFI rise (+0.053) that the claim matches is itself mostly this artefact. An ISG-robust marker rule gives -0.005/day (p 0.83).",
+      "Calibration against the gold-standard labels fails for the claimed contrast in 5/5 draws. Wave-1 slope ratio is 2.8-4.2. The slope of (generated - author) is significantly positive in every draw, while (generated - AIFI) is about 0. The trust layer passes only because generated and observed cells share the same biased classifier.",
+      "On the same generated bulk-only cells, an ISG-robust marker rule removes most of the wave-1 rise: slopes -0.003 to +0.039, not significant in draws 0-2. The AIFI slope on those cells is +0.037 to +0.080.",
+      "'Stays high after day 21' has no truth and no calibration. No paired COVID+ sample is later than day 20. All 16 bulk-only samples beyond day 21 are wave 1 and severe/critical (10 donors).",
+      "Nothing beyond bulk. Against the author truth, the bulk ISG score alone tracks within-donor change better than the generated share in every draw (wave 1: 0.61-0.68 vs 0.33-0.60). A bulk B-subset proxy ranks sample levels better (0.52-0.60 vs 0.12-0.48). The generated ISG coupling is 1.5-2.8x the true coupling (-0.46 to -0.85 vs -0.30 per SD).",
+      "The novelty is a misreading. The 'within-person inverse coupling to IFN' is classifier bias. Stephenson 2025 Fig 4A (PDF p.8) shows IFN-stimulated switched memory B cells raised in weeks 1-2: these are the cells AIFI mislabels as naive. Naive B up in week 1 vs COVID- is already reported (PDF p.3; Fig 2E, PDF p.5).",
+      "A real memory-share rise exists only in wave 2 (author labels +0.075/day; 15/15 donors). The bulk-only set has just 17 wave-2 samples (10 donors), and its wave-2 slopes were not significant.",
+      "Five-draw agreement is not five replications. All draws share seed 20260927 and the same bulk input. The post-hoc definition of the feature also inflates the screen q-values.",
+      "Minor: severity sign agreement for memB_share is 4/5 on all COVID+ test samples, and 3/5 in wave 1 <=21 d (draws 1 and 3 fail). The null claim of no severity difference is therefore not calibrated in every draw.",
+      "Housekeeping: this verifier wrote only to its own directory. It ran no GPU work and produced no paper-archive delta."
+     ],
+     "reasoning": "(1) Calibration. memB_share is not in calibration.csv, so I rebuilt it from composition.parquet. I reproduced the candidate's AIFI-vs-AIFI numbers: TIME_PASS holds in all waves and in wave 1. COVID+/- direction agrees in 5/5 draws; severity in 4/5 (3/5 in wave 1). In wave 1 the gate is weak: visit-to-visit sign agreement is 0.56-0.71, and the bootstrap CIs on the slope ratio are about 0.5-3.5.\n\nThe decisive test uses the cohort authors' own B-cell labels. They are built from CITE-seq CD27/CD11c and BCR isotype (Stephenson 2025 STAR Methods, PDF p.22), and they are stored cell by cell next to the benchmark labels. Against them, the generated wave-1 slope is 2.8-4.2x the truth in every draw. The trust layer passed only because generated and observed cells go through the same ISG-biased classifier.\n\n(2) Observed support. With the authors' labels, the wave-1 memory share does not rise (+0.015/day, n.s.; means 0.38 -> 0.35 -> 0.33). That holds in the LMM, within donors and per donor. An ISG-robust marker rule, validated against the author labels in every ISG quartile, agrees (-0.005/day). The AIFI 'observed rise' comes from AIFI calling ISG-high memory cells naive. The rate of that mislabelling falls from 18% to 1% between week 1 and week 3, and it reaches 45% for ISG-high IgG/IgA switched memory cells. A real rise appears only in wave 2, which the bulk-only set barely samples.\n\n(3) Biology and novelty. Stephenson 2025 reports more naive B cells in week 1 than in COVID- samples (PDF p.3; Fig 2E, PDF p.5). Its Fig 4A (PDF p.8) and text (PDF p.9) show IFN-stimulated naive and switched-memory B cells raised in weeks 1-2. The IFN-stimulated switched memory cells are exactly the population AIFI misreads. The claimed new 'memory-share trajectory coupled inversely to IFN' is therefore a labelling artefact, not new biology. Gisby 2022 reports only a plasma-cell module (tL, PDF p.5) and plasmablasts at day 14 (PDF p.7), so it has no B-subset trajectory to contradict.\n\n(4) Beyond bulk. The candidate already showed the generated slope vanishes after adjusting for bulk ISG. Against the author truth, the bulk ISG score tracks within-donor change better than the generated share, and a bulk B proxy ranks levels better, in every draw.\n\nThe paired and bulk-only bulk profiles match in wave 1, so the label artefact carries over to the bulk-only cells. Under the rule 'refuted if calibration or observed data contradict it', the claim is refuted. What survives is a label-level statement: the AIFI memory/naive call tracks the bulk IFN clock in both real and generated cells.",
+     "files": [
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/run.sh",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v01_calibration.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v01_calibration_all.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v01_calibration_time.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v02_bulk_scores.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v02_bulk_scores.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_author_labels.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_author_labels.log",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_crosstab.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_truth_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_label_drift.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_percell_isg.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_gen_vs_author.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v03_sample_shares.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v04_marker_check.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v04_rule_accuracy.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v04_misclassified_markers.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v04_rule_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v05_bulkonly_recompute.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v05_bulkonly_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v05_samecell.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v05_gt21.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v06_robustness.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v06_windows.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v06_donor_slopes.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v06_baseline_vs_author.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v07_contrast_tests.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v07_tests.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v08_transfer_check.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v08_transfer.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v09_switched_isg.py",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/verify/IMP-LONG-2_calib/v09_switched_isg.csv",
+      "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/cells_obs.parquet",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf",
+      "/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf"
+     ]
+    }
+   ]
+  }
+ ],
+ "lens_negative": [
+  {
+   "lens": "imp_clinical",
+   "agent_id": "a4d49cf3193c20654",
+   "negative_results": [
+    "Mortality (the main reason to use the 45 no-single-cell donors) gives no calibrated finding. The crude fatal-vs-survived associations among COVID+ are platelet ↑ (+0.80 to +1.60, q ≤ 0.045), 'Other' ↑, memory CD4 ↓ (−0.38 to −1.81), T ↓ and CD14 ↑. All of them disappear after adjusting for WHO severity at the sample: 0 of 50 features at q < 0.05 in any draw. Within severe/critical patients (73 samples, 28 donors, 12 fatal) there are also 0 of 50 in every draw. Observed fatal-beyond-WHO associations exist (Treg −2.51, p 1.3e-5; platelet +1.96, p 8e-5; memory CD4 −2.40, p 3.5e-4), but 7 of the 8 paired fatal donors are wave 2, and the generator does not reproduce them on paired test samples (platelet given WHO: gen +0.05 to +0.80 vs obs +1.69 to +2.03). The 6 fatal donors with no single cells are all wave 1. In them, platelet-fatal is significant in only 1/5 draws (+0.35 to +1.15). Their significant generated pattern is T ↓ and CD14 ↑ (p ≤ 0.014 in all draws), which is the LDG artifact (IMP-CLIN-5). Wave-1 fatal signals (7 donors: gdT ↓ 5/5 draws, memory CD8 ↓ 4/5) cannot be calibrated, because the paired set has only 1 wave-1 fatal donor. Files: s08_extra_models.parquet, s13_extra_summary.csv, s07_sensitivity.txt, s01_nosc_donors.txt.",
+    "Within-patient (longitudinal) severity dynamics are not detected. The within-donor WHO component is non-significant for all features (1 feature at q < 0.05, in draw 0 only), even though observed cells show within-donor platelet +0.64 (p 4e-4) and 'Other' +0.33 (p 0.007). The generator does reproduce the within-donor 'Other' effect on paired test samples (gates PASS in 4/5), so this is likely a design/power limit: 35 bulk-only donors contribute one visit. All bulk-only WHO associations are between-patient.",
+    "Generated features add no predictive value over the bulk. For donor-level peak severity and fatality, leave-one-donor-out L2-logistic AUC with age, sex and 10 bulk PCs is 0.743 and 0.770. Adding the generated platelet, Other, plasma, CD8aa or naive-B fraction gives 0.723–0.806, with no consistent gain (s12_fullbulk_cv.csv).",
+    "The COVID+ vs COVID− losses of T-cell subsets (l1 T, CD8aa, memory CD4, MAIT, gdT) pass calibration on all paired test samples but not under the wave-1 restriction. Observed wave-1-only results: T +0.11 (p 0.61), CD8aa −0.14 (p 0.52), memory CD4 +0.42 (p 0.003, opposite sign), MAIT −0.20 (p 0.44), gdT −0.21 (p 0.45). In the paired set these losses come from wave 2. Wave 2 also had a different single-cell processing protocol: its COVID− controls are 2020 samples processed with the 2021 protocol (Stephenson 2025 PDF p. 20). These contrasts therefore do not transfer to the bulk-only set, which is 88% wave 1, and are not reported.",
+    "Platelets/'Other' are higher in COVID+ vs COVID− in bulk-only generated cells (draws 0, 2, 4, q ≤ 0.005). Observed cells show no difference (overall p 0.11; wave 1 +0.21, p 0.57), so the gates are NULL_genfx: the generator creates this effect, and it is linked to the bulk LDG signal.",
+    "MS1 (IL1R2+) monocytes rising with severity (observed peak +1.56, p 1.3e-4; WHO +0.89, p 1.6e-5) is carried by draw 4 only (+1.27, q 4e-6). Draws 0–3 generate MS1 at 0.04–0.13× of observed and fail the gates. The MS1-gene bulk score is strongly associated (+1.15 SD, p 8e-9), tracks MS1 better than generated cells, and leaves no observed MS1 information beyond it (+0.09 SD).",
+    "The ISG-high T/monocyte positive control behaves: COVID+ and peak-severity contrasts pass calibration in nearly all draws (Spearman 0.72–0.89), and the observed effects are strong. However, the bulk ISG score tracks observed ISG-high fractions better (0.84–0.93), and the generated cells add nothing beyond it (gen given bulk ≈ 0).",
+    "Proliferating T (label) and proliferating NK are higher in COVID+, calibrated in wave 1 (proliferating T gates PASS in 5/5) and observed (wave 1 +1.39, p 3e-8). The bulk cycling score (MKI67, TOP2A, TYMS, CENPF, UBE2C, BIRC5) already shows the increase (+1.29 SD, p 3e-9), and the marker-based generated 'Cycling T/NK' state is made at only 0.23–0.59× of observed. Not reported as a finding.",
+    "CD56bright NK decreasing with peak severity is real in observed cells (−0.63, p 0.005; wave 1 −0.79, p 0.001). The generated fraction does not track it (Spearman −0.13 to 0.25; gates FAIL in 4/5), so the bulk-only signals in draws 1 and 4 cannot be used.",
+    "Age: no robust association. Only draw 3 has one (memory B −0.26 per decade, q 0.03). In observed cells memory B is −0.17 per decade (p 0.03), and B cells −0.25 per decade (p 0.004, q 0.19).",
+    "Sex: CD8aa is lower in males in draws 0–3 (q ≤ 0.011), and observed cells show the same (−0.57, p 0.018). This is treated as a labeller artifact, because the sepsis verification (SEP-CLIN-6) found CD8aa labels shift when the chrY labeller inputs are zeroed. Plasma cells higher in males (draws 3–4) and ILC lower in males (draw 0) are not robust.",
+    "Memory CD8 T and gdT decreasing with peak severity in bulk-only (draws 1–4, q ≤ 4e-4) has NULL gates: the observed peak effects are +0.22 (p 0.54) and +0.16 (p 0.57). They correlate with the LDG score and belong to the IMP-CLIN-5 artifact pattern.",
+    "Calibration transfer check of bulk scores, bulk-only minus paired within strata. LDG is higher in bulk-only severe/critical (+0.31, p 0.008) and COVID− (+0.46, p 0.01). The platelet, plasma, naive-B and B-cell scores are similar (|Δ| ≤ 0.34, p ≥ 0.1). Calibration therefore transfers for IMP-CLIN-1 to IMP-CLIN-3, but not for LDG-affected features (s12_transfer.csv)."
+   ],
+   "notes": "Lens: imp_clinical. Cohort: imperial_eskd. CPU only, run under systemd-run with MemoryMax=6G; no GPU work. All scripts and tables are in /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_clinical/, run in order s00–s20. Shared helpers are in c_common.py. s15_findings_numbers.txt collects every quoted number.\n\nDesign (fixed before looking at results):\n- Feature: logit((n+0.5)/(N+1)) for all 50 l1, l2 and state features.\n- Model: linear mixed model with a donor random intercept, run per draw, with BH correction over the 50 features within each draw × comparison × sensitivity.\n- Comparisons: COVID+ vs COVID− (adjusted for age and sex), and among COVID+ (adjusted for age, sex, days and days²): peak severity, WHO at sample, fatal, fatal within severe/critical, age, sex.\n- Extra models (s08): wave-1 only, fatal adjusted for WHO, and the WHO effect split into within- and between-donor parts.\n- Sensitivity sets: S1 drop the sex_flag sample; S2 drop each draw's training-donor samples; S3 both; S4 add a wave covariate; S5 days ≤ 21; S6 the 45 no-single-cell donors only; S7 donors with single cells only.\n- Alternative models: arcsine mixed model, donor-clustered OLS, donor aggregation, donor rank test.\n\nTrust layer:\n- The official calibration.csv rows are reported, but I also recomputed calibration donor-aware, with the same adjusted model and the same population restriction, on each draw's paired test samples.\n- Gates (s03 docstring): PASS = observed p < 0.05, same sign, effect ratio and level ratio within 0.5–2, Spearman ≥ 0.3. DIR = observed p < 0.10, same sign, level within 1/3–3, Spearman ≥ 0.2. NULL = no observed contrast. FAIL = anything else.\n- A draw 'carries' a result only if its gate is PASS or DIR and the bulk-only sign matches.\n\nKey cautions for the synthesizer:\n1. Wave is a single-cell processing batch (Stephenson 2025 PDF p. 20). Observed paired wave-1 and wave-2 cells differ strongly: COVID+ platelet fraction 0.14% vs 1.41%; wave-1 CD14 monocytes only 3.5–4.3%. The paired COVID+ set is 45% wave 2, but the bulk-only set is 88% wave 1. Use wave-1-restricted calibration (s13) for this cohort. Several all-sample calibrations (the COVID T-subset losses) do not survive it.\n2. The fresh-PBMC bulk contains LDG/neutrophil RNA that the cryopreserved single cells lack (Gisby 2022 PDF pp. 5, 18). The generator turns this signal into CD14 monocytes at the expense of T cells, and partly into platelets (IMP-CLIN-5). Adjust any severity or mortality association for the bulk LDG score.\n3. Draws share seed noise. The shared-noise share of cross-draw agreement within COVID+ is a median 22% for L2 types under 1% abundance and 3.8% for more common types (s14). Rare-type agreement counts are therefore not independent.\n4. statsmodels MixedLM with method='lbfgs' can return a degenerate optimum while reporting converged=True: llf = inf, coefficient 0, SE ≈ 2e15, p = 1. Reproduced in s20. My first s02 run had such fits; c_common.fit_mixed now fits both lbfgs and bfgs and keeps the best finite fit. Other agents using MixedLM should check for this.\n5. Of the 50 features, the simple bulk marker score tracks observed per-sample fractions better than the generated cells for 39 (s10 log). The cells add attribution, not information. The only robust cell-level additions here are naive B in COVID+ (IMP-CLIN-1) and, partly, the platelet–severity association (IMP-CLIN-3).\n\nArchive: as a write-restricted subagent I did not run the paper-archive update (maintenance/POLICY.md). There is no archive delta from this lens; the coordinator owns that step."
+  },
+  {
+   "lens": "imp_programs",
+   "agent_id": "ae50f8fd587cc7271",
+   "negative_results": [
+    "TNF falls in monocytes in observed cells (Stephenson 2025 PDF p.6, its top severity gene): CD14 -0.86 for severity (p 0.006) and -1.23 for fatal (p 0.0003). The generated cells do not reproduce it. Gates are only SIGN-ONLY (ratio 0.09-0.89), bulk-only effects are -0.38 to +0.11, and no draw carries it. The paired truth is driven by wave 2: bulk-only bulk TNF is -0.88 in wave 2 vs wave 1 (p 0.006), and bulk-only bulk fatal is +0.04 (p 0.95) against -0.80 (p 0.001) in the paired bulk. Calibration does not transfer (programs_summary.csv, transfer_contrast_agreement.csv, transfer_wave_effect.csv).",
+    "ISG vs severity: observed paired cells show +1.0 to +1.9 in most types (p <= 0.01), and the paired-test gates pass. But the bulk-only generated cells show only +0.15 to +0.94 (no draw has q < 0.05), and the bulk-only bulk shows +0.63 (p 0.11) against +1.51 (p 0.0003) in the paired bulk. Severe paired samples are +0.82 higher in bulk ISG than severe bulk-only samples, and wave 2 adds +1.0 to +1.2. The severity-ISG truth reflects early wave-2 sampling, so no claim is made.",
+    "Granulocyte/LDG genes (Gisby tB module; severity- and fatal-up bulk genes ELANE, ARG1, MMP9, PADI4, ANXA3, DAAM2) have no single-cell counterpart: the trust-layer LDG state ratio is 0.01-0.03. Observed paired totals do not rise (ELANE -0.04, MMP9 -0.43, DAAM2 -0.30 log2). The generated cells assign these genes to monocytes or T/NK cells inconsistently. In severe and fatal patients the bulk-only granulocyte bulk score is 0.7-1.4 log2 above the paired one (fatal q 0.011). These genes cannot be attributed, and reading them as monocyte expression is an artefact.",
+    "Attributing severity and fatal bulk DE genes to cell types does not beat simple baselines. Severity top-up, paired-test carrier agreement: 0.40-0.53, against 0.50-0.67 for the abundance reference. Fatal top-up: 0.67-0.80, against 0.90-0.97 for a constant 'monocyte' guess. Top-down lists: 0.37-0.80, against 0.40-0.70 for the reference. In observed cells 15 of 17 severity bulk-up monocyte genes are expression-driven, but generated cells call only 1-4 of 14-15 expression-driven. The reason is that the generated cells over-produce CD14 monocytes in severe patients (+0.022 to +0.060 share, against -0.010 to +0.008 observed; sign wrong in draws 1-2) (attribution_calib.csv, driver_check_summary.csv).",
+    "LRRC15, the headline of Gisby 2022 (a plasma protein, PDF p.2 and p.10), is not on the 6k panel. Neither are the immunoglobulin genes, XBP1, ORM1, CEACAM3/4/6/8, OSM, GSR, G6PD, histone genes, CD59, C4BPA, F12, DDX58, STAT3 and TGFB1. None of these can be tested in the generated cells.",
+    "Plasmablast/antibody and proliferation programmes inside B (L1) and plasma cells cannot be reported. Bulk-only effects in B (L1) are +0.29 to +1.67, but the observed within-type contrasts are null, so every gate is UNINFORMATIVE. Plasma cells have at least 10 cells in only 8-54 % of generated samples. The plasmablast signal is a composition effect (IMP-PROG-5).",
+    "T-cell programmes cannot be reported. T activation in memory CD8 (COVID) passes its gates, but only draw 2 reaches q < 0.05, and the within-sample memory-vs-naive CD8 interaction fails (observed -0.44, p 0.003; generated -0.05 to +0.26). T exhaustion is FAIL or UNINFORMATIVE. Gisby's ILC-family genes (IFNG, GATA3, RORA) fall in the bulk (-0.65), but the observed within-type changes are small, the gates are UNINFORMATIVE in all draws, and the generated cross-type profile does not match (r -0.56 to 0.14).",
+    "Platelet negative control: generated monocytes gain platelet transcripts with COVID, severity and fatal outcome (+0.1 to +0.95; several p < 0.05). Observed monocytes do not for COVID or severity (p 0.3-0.8), and no draw passes. This is a generator artefact.",
+    "The fatal contrast has a weak trust layer. The paired fatal truth comes from 7-8 donors, 28 of whose 31 samples are wave 2, and there is only one wave-1 fatal paired donor. Bulk-only fatal donors are 7 wave-1 and 5 wave-2. They are more lymphopenic (bulk T markers -1.12, against +0.01 in the paired set) and carry more granulocyte signal. Fatal vs survived within severe/critical patients (12 vs 16 bulk-only donors) cannot be calibrated (about 7 vs 5 test donors).",
+    "The double-centred agreement (IMP-PROG-2) is not specific to one contrast. The generated COVID pattern correlates with the observed severity pattern (0.10-0.12) about as well as with the observed COVID pattern (0.10-0.14).",
+    "There is a gene-length technical axis. Generated within-type COVID logFC correlates with gene span (Spearman 0.07-0.21 in draws 0-2), where observed does not (0.01-0.05). Observed fatal logFC is also length-correlated (0.14-0.24). The long-gene 'bulk-discordant' fatal genes (MGA, UBR1, TTN, VPS13D and others) are therefore suspect as technical (tech_length.csv).",
+    "Two HLA-II side results fail. In cDC2 the COVID effect is uninformative (observed -0.08, p 0.15), and only draw 3 carries it. In CD16 monocytes the generated cells lose HLA-II with severity in draws 0-2 (-0.15 to -0.17), but observed CD16 monocytes do not (+0.05).",
+    "COVID monocyte composition fails. In calibration.csv, the l1 Monocyte share for COVID+ vs COVID- is -0.009 to -0.029 observed but +0.024 to +0.075 generated in draws 1-4: the sign is wrong in 4/5 draws. MAIT is under-generated (at least 10 cells in 8-23 % of generated samples vs 54 % observed), so no MAIT programme analysis is possible."
+   ],
+   "notes": "Lens imp_programs, cohort imperial_eskd. All work was CPU only (systemd-run scope MemoryMax=6G, no swap, CUDA_VISIBLE_DEVICES empty); no GPU was used, and subsample.npz was never loaded. Every script, log and table is in /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_programs/: s00-s16 with their .log/.csv/.parquet outputs, ip_common.py, and text extracted from Gisby 2022 and Stephenson 2025.\n\nDesign, pre-registered in ip_common.py and the script docstrings:\n- Pseudobulk per sample x type with at least 10 cells, edgeR-style log2 CPM.\n- Every contrast is donor-aware: donor means, then Welch.\n- Contrasts: COVID+ vs COVID-; severity_peak severe/critical vs mild/moderate among COVID+; fatal vs survived among COVID+.\n- Each draw is analysed separately and gated by its own paired-test calibration, recomputed on the same restriction (COVID+ only for severity and fatal) and also on wave-1 samples only. Thresholds are the same as in the sepsis programs lens: gene-level PASS needs r >= 0.30 and top-100 sign agreement >= 0.70; a programme PASS needs an informative observed contrast, the same sign, Spearman >= 0.20 and a ratio of 0.25-4.\n- A draw carries a result only if it passes the gate and has a drawwise BH q < 0.05.\n\nCaveats that apply to every finding:\n1. All five draws share the sampling seed, so agreement across draws is not independent replication. I relied on per-draw gates.\n2. The observed paired samples used as truth (honesty item 3) overlap about 95 % with each draw's calibration test set (item 1).\n3. 88 % of bulk-only samples are wave 1, but half of the paired samples are wave 2 (dexamethasone era, systematic early sampling). The paired severity and fatal truth leans on wave 2. Calibration-transfer checks (transfer_*.csv) support transfer for HLA-II and the monocyte alarmin/MS1 programmes, but not for severity ISG, TNF or the granulocyte, platelet and T-composition features.\n4. Generated MS1 cell states sit at 0.04-0.37x of observed, so the monocyte findings concern gene programmes, not discrete states.\n5. Beyond-bulk baselines used throughout: a bulk score of the same genes (per-sample partial Spearman and a likelihood-ratio 'adds to bulk' test), the abundance-only reference, a constant prior, and NNLS/csSAM deconvolution (s05b; NNLS fractions correlate with observed UMI shares at 0.02-0.76 depending on type).\n\nOverall: the bulk-only generated cells reproduce known COVID/ESKD within-type programmes in the right cell types, best of all monocyte/cDC2 HLA-II loss with B-cell sparing. The one consistent piece beyond the bulk is a modest cell-type-specific response pattern that deconvolution cannot recover. No new biology was established.\n\nRegistration under paper-release-20260928/maintenance/POLICY.md is left to the coordinator; this agent wrote only inside its lens directory."
+  },
+  {
+   "lens": "imp_longitudinal",
+   "agent_id": "a7066f68a7c0ebb72",
+   "negative_results": [
+    "Monocyte recovery is not reproduced. Observed wave-1 slopes: l1 monocyte +0.106, CD14 +0.082, CD16 +0.114, intermediate +0.091 logit/day (all p <= 0.002). No wave-1 temporal gate passes: generated paired-test wave-1 slopes are -0.019 to +0.048, slope ratios -0.2 to 0.6. Bulk-only wave-1 slopes are -0.021 to +0.061, mostly not significant. Bulk-only early samples also have a higher CD14/alarmin bulk score than paired early samples (+0.72 SD, p 0.001), so calibration does not transfer (s15, s16, s08).",
+    "CD16 monocytes follow opposite courses by wave in observed cells. Wave 1: +0.114/day (p 2e-4). Wave 2: -0.072 (p 0.004), and -0.112 in severe wave-2 donors (p 0.005). The wave-2 decline fits Gisby 2022's wave-2 flow result (PDF p.7) and the dexamethasone era. The generator reproduces neither course (paired-test wave x day -0.034 to +0.053, not significant) (s15).",
+    "Severity x time interactions do not replicate in bulk-only donors. In observed cells, ISG declines faster in severe donors (q 0.046 monocyte, 0.036 T) and the memory share rises faster (+0.10, p 4e-4). Bulk-only: ISG-monocyte severity x day +0.000 to -0.023, memory share -0.003 to -0.021. Split by wave, the observed effects come from wave 2: memory share +0.008 in wave 1 (p 0.84) vs +0.142 in wave 2 (p 1e-4); ISG monocyte -0.136 (p 0.14) vs -0.116 (p 0.05). The bulk ISG score of bulk-only wave-1 donors shows no interaction (-0.002, p 0.9). The 17 wave-2 bulk-only samples do reproduce the memory-share interaction (+0.12 to +0.20), but they come from 10 donors who also have paired data, 6 of them training donors of some draw. After excluding training donors it holds in only 3 of 5 draws (s05, s10, s19, s20, s20b).",
+    "The early (d0-7) ISG difference by severity is attenuated in bulk-only donors. Wave-1 observed: +3.41 logit (p 0.02; 5 vs 11 donors). Bulk-only wave 1: +0.46 to +1.49 (p >= 0.11; 9 vs 14 donors). The bulk ISG score of bulk-only donors shows the same attenuation (+0.64 SD, p 0.17). Fatal-early comparisons are impossible in wave-1 paired data (1 fatal donor) (s18).",
+    "The Treg rise is not a wave-1 bulk-only finding. In observed cells it comes mostly from wave 2: +0.017/day in wave 1 vs +0.086 in wave 2 (wave x day p 0.007). The wave-1-gated draws (1, 3) give +0.002 and +0.019 (not significant); draws 0 and 4 overshoot (+0.079, +0.047). The generated slope disappears after ISG adjustment, while the observed slope keeps +0.032. The bulk FOXP3/IL2RA/CTLA4 score tracks the observed Treg level better than the generated cells (Spearman 0.60 vs 0.46) (s07, s11, s15, s16).",
+    "The pDC decline is a wave-2 effect in observed cells (wave 1 -0.027, not significant; wave 2 -0.125, p 2e-9). Bulk-only wave-1 slopes (-0.009 to -0.051) agree with the weak wave-1 truth, but no wave-1 gate passes (Spearman 0.01-0.18), so no claim.",
+    "DN T cells fall in wave 1 in both observed cells (-0.073/day, p 2e-4) and bulk-only cells (-0.040 to -0.100; W1_TIME_PASS in draws 0,1,3). Not reported: their level is over-generated (calibration.csv ratio 2.3-3.3), and the decline is fully IFN-coupled (ISG-adjusted observed slope -0.009, p 0.71).",
+    "MS1 monocytes fall in wave 1 in observed cells (-0.090/day) and bulk-only cells (-0.043 to -0.092). Not reported: generated MS1 levels are 0.04-0.37x the observed levels, so the feature fails the trust layer.",
+    "Several bulk-only trends are imposed by the generator and are absent in observed wave-1 cells. Six-type NK: -0.021 to -0.044/day generated vs -0.001 observed. Proliferating T: -0.05 to -0.07 generated vs -0.021 (not significant) observed; generated slopes are 2.7-5.4x observed on paired-test samples. Memory CD4 T in draws 0 and 4: +0.075 and +0.067 generated vs -0.008 observed (s15, s16).",
+    "Plasmablasts trend in opposite directions by wave in observed cells (wave 1 -0.063, p 0.027; wave 2 +0.052). The wave-1 gate passes only in draw 1 (bulk-only -0.061, q 0.002). The bulk-only early-severity excess (+0.35 to +1.35) survives adjustment for a bulk plasma-cell score that does not itself separate severity groups in bulk-only donors. Not trusted (s16, s18).",
+    "MAIT cells are depleted early in severe donors: wave-1 observed -0.85 (p 0.046), bulk-only -0.51 to -1.21. Not reported: the effect is bulk-derivable (bulk MAIT score -1.22 SD, p 0.004), and MAIT level calibration fails (calibration.csv ratio 0.41-0.66) (s18).",
+    "Peak timing: no generated trajectory has a mid-course peak that the observed data support. The observed quadratic peak of proliferating NK near day 3 is not resolved. In bulk-only cells the memory share plateaus after week 2, while it keeps rising to day 20 in observed cells (s12_vertex, s11).",
+    "Rare states that fail calibration have no trajectory claim: C1Q-high monocyte, LDG, cycling monocyte, DN2/ABC B, HSPC, cycling activated T (Spearman about 0, or ratio <= 0.2). Six-type DC, CD56bright NK and CD56dim NK show no reliable trend."
+   ],
+   "notes": "Lens: imp_longitudinal. All outputs, the 21 scripts and every quoted table are in /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_longitudinal/. s21_findings_numbers.txt gathers every number quoted above, read directly from the result tables. Work was CPU only, run through systemd-run with MemoryMax=6G; no GPU work was started. No archive delta was produced: only analysis files in the lens directory were written, and any PBISC-Diffusion archive update is for the coordinator to decide.\n\nMethods.\n- Time is days since the first positive swab. It matches bulk_samples.csv exactly. Using the onset-based definition (Stephenson's) gives the same slopes.\n- Models: MixedLM on logit(fraction) with a donor random intercept, fitted by ML, with linear, quadratic (B-spline degree 2, as in both papers) and window terms.\n- Robustness checks: within-donor fixed-effect slopes, per-donor Wilcoxon tests, severity and age/sex/ethnicity adjustment, exclusion of training donors and the sex-flag sample, and the no-single-cell donor subset (31 donors, all wave 1, with no training-donor overlap).\n- The trust layer was recomputed on COVID+ paired test samples (s03), and again restricted to wave 1 (s16). Gates were fixed before the bulk-only results were read: level ratio 0.5-2 with Spearman >= 0.3, same slope sign, slope ratio 0.5-2, and within-donor tracking > 0.\n\nMain confounder: wave.\n- Bulk-only COVID+ samples are 89% wave 1; paired samples are 45% wave 1.\n- Several observed trajectories differ by wave: faster ISG decline and memory-share rise, plus Treg, pDC, CD16/intermediate monocyte and plasmablast trends. Wave-2 severe patients received dexamethasone (Stephenson 2025 PDF p.11), and there is no treatment variable.\n- Bulk-only severe donors are first sampled later (median day 9 vs 4).\n- Bulk-only trajectories are therefore judged against wave-1 observed cells.\n- The severity x time interactions reported by Stephenson (pooled waves) are wave-2 effects in this paired subset. Their absence in the wave-1 bulk-only donors is expected, not a generator failure.\n\nOverall answers.\n- Generated bulk-only trajectories match the observed paired trajectories, in shape (monotone from week 1) and in wave-matched magnitude, for the IFN-coupled features: ISG states, B memory share, proliferating NK, and cDC2.\n- The generator fails the observed wave-1 monocyte recovery and the wave-dependent CD16 course. It also imposes declines in platelets, NK and proliferating T that the observed cells do not show.\n- Donors without single cells show the same dynamics, often slightly steeper.\n- No state's early level or slope differs by outcome in bulk-only donors in a way that is calibrated, supported by observed cells and goes beyond bulk. The early differences that do replicate (MAIT, CD8aa) come from the bulk, and the slope differences are wave-2 only.\n\nCaveats.\n- All five draws share the generation seed and the bulk input, and their calibration test sets overlap by about 90%. Draw agreement is therefore not five independent replications; per-draw gates are reported instead.\n- memB_share was defined post hoc.\n- Observed paired data stop at day 20, so anything after day 21 is generated only.\n- Every positive temporal finding here is predictable from the bulk ISG score (the IFN clock). The generated cells add the attribution of that clock to specific cell types, not new temporal information."
+  },
+  {
+   "lens": "imp_populations",
+   "agent_id": "a37b9b3cf6e8ac972",
+   "negative_results": [
+    "IFN-negative HLA-II-low CD14 monocytes (the sepsis MS1-like / Schulte-Schrepping HLA-DRlo S100Ahi state; joint cluster Mono r2.0 c17, Jaccard 0.26-0.29) do not expand in ESKD COVID. Observed: COVID -0.078 (p=0.0023), severity -0.075 (p=0.020). The generator over-produces them (ratio 1.66-2.12) and bulk-only effects are negative or not significant (severity 1/5 draws p<0.05). This differs from the sepsis run, where the HLA-DR-low shift was IFN-independent.",
+    "Glucocorticoid-associated 'dex. monos' of Stephenson 2025 are not reproduced. Rule: CD163 plus >=1 of ADAMTS2/VSIG4/SLC1A3/MFGE8/MRC1. Observed: 11.9% of monocytes in wave-2 severe/critical samples vs 1.1% in COVID-. Generated: 0.02-0.09x of observed; the dex genes are emitted in <=0.7% of generated monocytes vs 0.7-4.2% observed (tables/s10_*).",
+    "The C1Q+ CD16 monocytes of Stephenson 2025 (C1 CD16) cannot be analysed: the trust-layer state 'C1Q-high monocyte' has ratio 0.06-0.60 and Spearman -0.02 to 0.15.",
+    "Activated HLA-DR+ NKG7+ GZMH+ CD8 T cluster (T r2.0 c32): unstable (re-embedding Jaccard 0.16-0.25), over-produced (2.6x by kNN mapping), doublet-like flag, and no clinical association (|diff| <= 0.007).",
+    "Effector/atypical B cluster (B r1.0 c13; observed FCRL5/FGR/FCRL3): calibration Spearman -0.11 to -0.06, ratio about 1.7, no association.",
+    "S100A8/9+ 'DC3-like' cDC2 (DC r1.0 c3): fatal observed +0.081 (p=0.006); bulk-only 4/5 p<0.05, gated in draws 1-4. Not reported: only about 5 DCs per sample in the subsample, and no embedding-free confirmation is possible because S100A8/9 are not in markers.npz.",
+    "Platelet sub-cluster (Other r1.0 c0; TSC22D1/HIST1H2AC/TUBB1): fatal observed +0.28 (p=0.013), bulk-only 3/5. Parent counts are about 3 cells per sample and calibration Spearman is 0.24, so it is not testable reliably. It is a split of the L2 'Platelet' label.",
+    "IEG-high (FOS/JUN/DUSP1) NK subset (NK r1.0 c7) is lower in COVID+ (observed -0.19, p=0.0013; bulk-only 3/5). Likely a sample-handling signature (inpatient vs outpatient processing), not reported as a population.",
+    "CD16 monocyte loss (Mono r1.0 c5/c8) and memory CD4/CD8 subset changes (T r1.0 c3/c8/c12) are significant and gated, but they re-find AIFI L2 labels (CD16 monocyte, Memory CD4/CD8 T), so they are not new populations. Plasma cells (B r1.0 c29, the observed ASC rise, COVID+ +0.062, p=0.002; gated draws 1-3) are the L2 'Plasma cell' label.",
+    "'ISG+ HLA-II-low' is not a coupled cell state. Within samples, ISG-high CD14 cells are slightly less often HLA-low (observed/expected 0.75-0.87; log OR about -0.2 to -0.5), and this coupling does not change with severity (observed p=0.30). The subset arises because both axes shift together.",
+    "Severity/fatal effects of ISG states in NK, B and T cells are strong in observed cells (p 0.001-0.003) but are not reproduced in bulk-only cells (0/5 draws for severity; fatal effects vanish after adjusting for days since swab). Bulk-only severe samples were taken later and have lower bulk ISG10 than paired severe samples (-0.89 log2, p=0.041).",
+    "Fatal vs non-fatal within severe/critical patients is not supported. The bulk-only effect for ISG+ HLA-II-low (+0.14 to +0.19, 5/5 p<0.05) is not significant in observed cells (+0.137, p=0.13, 8 vs 5 donors), and the generated within-severe contrast is about 0 in draws 3-4 (gate fails in draw 3).",
+    "No generated fraction consistently adds outcome information beyond bulk scores (ISG10, HLA-DRA/DRB1, HLA-DP/DQ/DM/CD74, CD163, monocyte and CD16-monocyte genes). Where generated cells appear to add (all-monocyte HLA-II-high, draws 1-4 p 0.004-0.041), the observed counterpart does not (p=0.38). Only the observed CD163+ CD14 fraction adds beyond bulk (severity LR p=0.0043).",
+    "Domain gap: a plain joint embedding separates domains. The generated share among observed cells' kNN neighbours is 0.38-0.66, against 0.875-0.93 expected; per-domain z-scoring restores it to 0.72-0.85. Generated cells detect fewer genes at similar depth (monocytes: 226-315 vs 387 genes per cell). Per-domain z-scoring pushes cluster-level generated/observed ratios toward 1, so every reported population is re-defined by an embedding-free rule.",
+    "Cluster-screen flags. r1.0: 25 of 63 screened clusters flagged (12 generated-only with obs_enrich < 0.35, 7 library-driven, 15 marker-discordant). r2.0: 94 of 161 flagged (71 marker-discordant micro-clusters). Library-driven clusters (e.g. Mono r2.0 c16/c19/c25 at 1.5-1.8x library; Other c6/c7 at about 3.8x) are not populations.",
+    "Several cluster-level candidates are unstable on independent re-embedding (seed 1, 2,000 HVGs): Mono r2.0 c12 (Jaccard 0.25), c17 (0.26-0.29) and T r2.0 c32 (0.16-0.25). Only embedding-free versions are reported.",
+    "Draw agreement is not independent: all draws used seed 20260927, and the subsample takes the same cell slots in every draw. Same-slot state odds ratios across draws are 11.9-52.6, against 1.5-7.6 at shifted slots."
+   ],
+   "notes": "Lens imp_populations (imperial_eskd), CPU only. Every script ran via run.sh (systemd-run scope, MemoryMax 6G, MemorySwapMax 0, CUDA_VISIBLE_DEVICES empty, 2 BLAS threads), after checking MemAvailable >= 48 GiB. Python was the igraph runtime_env (scanpy 1.11.5, statsmodels 0.15.0). Nothing was written outside /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bulkonly_lora5_20261006/analysis/wf/imp_populations/ (scripts s00-s15 at the top level, tables/, logs/). No GPU work, no paper-asset changes and no archive update: these are new analysis tables only, and the coordinator decides whether any enter the PBISC-Diffusion archive.\n\nPipeline:\n- s01: within-parent joint embeddings (Monocyte, T, NK, B, DC, Other) of the 200-cell subsamples of observed, paired_gen (5 draws) and bulkonly (5 draws). 3,000 seurat_v3 HVGs with batch_key = set, kept only if detected in >= 5% of cells in both domains. Per-domain z-scoring, PCA 30, kNN 15, igraph Leiden at r 0.5/1/2. Observed cells were also kNN-mapped onto the generated clusters.\n- s02: cluster profiles and markers in each domain.\n- s03-s05: per-draw donor-aware screen of every cluster over COVID+ vs -, peak severity, fatal, fatal-within-severe and WHO grade, with BH. Per-draw calibration on the paired test samples, recomputed on each contrast's own restriction. Gate = same contrast sign, Spearman >= 0.2, level ratio 0.5-2. Artefact flags.\n- s06: independent re-embedding with seed 1 and 2,000 HVGs (Jaccard).\n- s07: embedding-free marker rules on all cells (markers.npz), with observed cells binomially thinned per draw. Tests: donor-level Mann-Whitney, BH over 17 rules, donor-clustered OLS adjusted for days since swab, age, sex and wave (MixedLM did not converge reliably and was replaced), wave-1-only analysis, and a sensitivity set without the sex-flag sample and training-donor samples.\n- s08/s08b: bulk baselines and likelihood-ratio increments for generated vs observed fractions, plus a calibration-transfer check.\n- s09: ISG x HLA coupling. s10: dex-monocytes. s11: artefact clusters. s12: shared sampler noise. s13: marker coherence of the rules. s14: training donors per draw. s15: every quoted number.\n\nMain conclusion: no new population is found. The candidate with the best support is IMP-POP-1, an IFN-stimulated HLA-II-low CD14 sub-state. Its calibration is good in all 5 draws and its associations with severity and WHO grade replicate in observed cells, including wave 1 and after adjustment. However, it re-expresses bulk ISG and HLA-DR signals: neither the generated nor the observed fraction adds to bulk scores. Draw agreement is inflated by shared seeds.\n\nKey caveats:\n- Every draw contains at least one severe, often fatal, training donor rich in these states (s14), e.g. draw 4's C33 has 53% ISG+ HLA-low and 74% CD163+.\n- The generator shifts CD14 HLA-II levels down, so IMP-POP-1's ratio near 1 partly comes from errors that cancel.\n- Bulk-only severe samples were taken later than paired ones (median 15 vs 9 days), which weakens ISG severity contrasts.\n- Fatal outcome in the paired truth is concentrated in wave 2 (28/31 fatal samples), so dexamethasone confounds the truth for fatal-related monocyte states. Wave-1-only observed analyses were used where possible; fatal cannot be tested in wave 1 truth (one donor)."
+  }
+ ],
+ "provenance": {
+  "source": "/home/sj_server_1/.claude/projects/-home-sj-server-1/cd3948fe-c9dc-46c9-874d-211078d8ba7a/subagents/workflows/wf_d188d252-beb/journal.jsonl",
+  "source_sha256": "7929331f86b4d41f2f0c62dca264882f11c79214ae1c4f3e0d4285d67cab00b2",
+  "workflow": "wf_d188d252-beb",
+  "n_journal_rows": 94,
+  "note": "verbatim discover results (findings, negative_results, notes) and verify results",
+  "scripts": [
+   "synthesis_imperial_eskd/i00_counts.py",
+   "synthesis_imperial_eskd/i01_calibration_summary.py",
+   "synthesis_imperial_eskd/i02_extract_graded.py",
+   "synthesis_imperial_eskd/i03_checks.py",
+   "synthesis_imperial_eskd/i03b_recovery_and_training_samples.py",
+   "synthesis_imperial_eskd/i04_build_findings_json.py",
+   "synthesis_imperial_eskd/i05_spotcheck.py"
+  ],
+  "gpu": "none (CPU only, systemd-run MemoryMax=6G, no swap)",
+  "cited_files_check": "synthesis_imperial_eskd/i02_cited_files.csv: 1,636 citations, 1,500 unique files, 0 missing",
+  "archive": "no archive delta from this synthesis agent; registration under PBISC-Diffusion POLICY.md is left to the coordinator"
+ }
+}
\ No newline at end of file