Datasets:

junidude14
/

PBISC-data

paper-releases/pbisc-diffusion/updates/20261013T094815Z-all-celltypes-final-palette-142/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/STRENGTHENING_PROPOSAL.md ADDED Viewed

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+# Figure 1–6 / Supplementary 근거 보강 제안
+> **페이지 번호 안내:** 이 문서의 쪽수는 재배치 전 131장 덱 기준이다. 실제 PPTX/PDF는 사용자 정의에 맞게 재배치됐으며, 현재 쪽수는 [페이지 대응표](../celltypes_palette_master_20261013/page_map.csv)와 [수정 보고서](../celltypes_palette_master_20261013/README.md)를 따른다.
+2026-10-13. 사용자의 새 Figure 정의를 기준으로 한 **논의용 제안**이다. 새 실행 승인이나 기존 campaign의 변경 지시가 아니다. 이번 검토에서는 학습·추론·scoring·그림 생성을 실행하지 않았다.
+## 우선순위와 중복 방지
+가장 큰 보강 효과는 Figure 4의 환자 단위 비교, Figure 5의 독립적인 rare-state identity 확인, Figure 6의 구체적인 논문 결과 재현에서 나온다. Figure 1–2는 기존 자료의 사용 범위와 비교 조건을 명료하게 보여주는 일을 먼저 한다.
+- **기존 자료 재구성:** corpus/exposure 지도, SLE 공통 축 ablation, healthy full FT/rank 비교, 누락된 scSemi CV-CAT, robustness summary.
+- **이미 진행 중인 일의 완료:** strict total-support curve 및 질환 patient-aware 비교. 별도의 중복 campaign을 만들지 않는다.
+- **새 CPU 분석·그림:** paired rare-state 정성 패널과 독립 marker 검증, 기존 bulk-only 결과의 논문 endpoint 재현. 분석의 새로움과 model inference의 새로움을 구분한다.
+- **조건부 새 GPU 작업:** 기존 근거로 해소되지 않는 명시적 질문이 있을 때만 제한된 sampling/adapter-seed 보완 또는 adapted-model input intervention. 아래 기본 계획에는 새 대규모 학습을 넣지 않는다.
+검토 중 상태(13:58 KST 전후): server_1은 Imperial draw1의 scSemi target, server_2는 healthy fold4/n10의 scSemi target을 처리 중이었다. 질환 완료 score receipts는 Sepsis 5 draws + Imperial draw0이며, 최종 cross-draw 통계는 아직 없었다. Healthy는 20개 support unit이 이미 계획되어 있고, 로컬에서 11개 generation triples 및 n5 두 unit의 scoring summary를 확인했다. 이는 시간에 따라 변하는 관측이며 새 실행 전에 live receipts를 다시 확인한다.
+## Figure 1 — 규모뿐 아니라 데이터 사용 경계를 보여준다
+**추가할 그림:** corpus 구성 통계 옆에 `pretraining / calibration reference / paired adaptation / held-out paired evaluation / bulk-only application`의 데이터 사용 지도를 둔다. 상세 donor·sample·study 교차표는 Supplementary Table로 보낸다.
+**이유:** corpus 표는 이미 있지만, 어떤 데이터가 언제 모델과 평가에 노출됐는지를 한눈에 볼 수 있어야 한다. 기존 corpus 문서에는 27명이 내부 corpus split을 가로지르는 기록이 있다(26 GSE274680, 1 GSE132802; 38 validation/test samples). 이는 **외부 SLE/paired test 환자의 누출을 입증하는 자료가 아니다**. AIFI reference의 85/108 donor가 사전학습에 포함된 것도 calibration reference 노출과 target-patient 중복을 구분해야 한다.
+**작업:** metadata와 최종 checkpoint/corpus 계약을 연결하고, 표를 `분리 확인 / 중복 확인 / identity 미확인`으로 구분한다. Sample 불일치만으로 donor 독립을 주장하지 않는다. 실제 validation recipe에 해당 세포가 사용됐는지는 별도 linkage 문제다.
+**완료 기준:** 각 성능 패널에서 train/support/reference/test의 데이터 접근 범위와 고유 donor 수를 추적할 수 있다. 알려진 내부 corpus 중복을 근거 없이 외부 benchmark 누출로 확대하지 않는다. 실제 target 중복이 발견되면 기존 출력에서 평가 대상을 재정의할 수 있는지 먼저 확인한다.
+**종류:** metadata audit + 도표. 새 학습 없음. 현재 9·11·16쪽 보강.
+## Figure 2 — 동일 조건의 SLE 비교와 불확실성
+**추가/개선할 그림:** architecture와 gene context를 두 panel family로 분리한 점·오차막대 plot. 오차막대는 기존 donor L1 분산과 sampling-seed 변동 등 근거가 있는 경우에만 표시한다. 단일 pooled P/C 값에는 기존 CI가 없으며, 필요하면 donor-aware CPU 분석을 별도 작업으로 둔다. 공통 SLE 42 donors / 1,006 genes로 이미 계산된 P/C/composition을 사용하고, 기존 logFC/DEG benchmark는 별도 gene-axis 정보를 함께 표시한다. Real-versus-real sampling control을 기준선으로 둔다.
+**이유:** architecture의 1% detection floor와 gene-panel의 0.1% floor를 합쳐 순위를 매기면 비교 조건이 흐려진다. 6k의 5 seeds는 **sampling seeds이며 독립 pretraining 5회가 아니다**.
+**완료 기준:** architecture 내부, gene-context 내부 비교가 각각 동일 평가 조건을 쓰고, donor·sampling seed·training seed의 수를 구분한다. Development benchmark에서의 선택을 독립적인 외부 검증으로 부르지 않는다.
+**종류:** 기존 결과 재시각화. 13–15���과 `ablation_pretrain_*` source tables 재사용. 모든 architecture를 재학습할 필요는 없다.
+**조건부 추가:** 특정 architecture/gene panel의 안정적 우위를 논문의 주요 주장으로 삼아야 할 때만 가장 가까운 경쟁 checkpoint에 sampling seeds를 보완한다. 학습 안정성 자체의 주장은 별도의 training-seed 반복이 필요하며 sampling 반복으로 대신할 수 없다.
+## Figure 3 — real-bulk 문제와 적은 paired 수로 해결되는 범위
+**3A: 같은 평가 기준의 before/after.** Healthy paired raw zero-shot, calibrated zero-shot, full FT, LoRA를 한 축에 나란히 표시한다. 지표는 precision, coverage, composition L1을 각각 유지한다. 23쪽 native-depth 진단과 116쪽 pooled FT benchmark 수치를 직접 연결하지 않는다.
+**3B: rank 선택의 근거.** r4/r16/r64/full FT에 대해 parameter–quality plot을 만든다. Precision과 coverage를 분리하고 composition error를 함께 보여준다. r16은 성능·파라미터 절충안으로 평가하며, r16을 이기게 하려고 사후 가중 종합점수를 만들지 않는다.
+**3C: strict total-support curve.** 5/10/20/40 pairs × 5 folds, 추가 validation 없음, fixed 3,000 steps의 기존 campaign을 완료한다. 각 method에 동일 support/test와 허용된 feature-selection 정보를 적용한다. 49쪽의 n+7 validation 곡선은 historical sensitivity, 54쪽은 기존 exact-five 근거로 유지한다.
+**완료 기준:** 총 접근 paired 수가 x축과 일치하며, 동일 donor의 방법 간 차이를 보여준다. 불확실성은 support/seed variation과 held-out donor variation을 구분한다. 최종 결론은 해당 fixed-step 학습 절차에서의 데이터 효율이지 모든 최적화 절차의 최소 paired 수가 아니다.
+**종류:** 3A/3B는 기존 결과 재구성. 3C는 **기존 진행 작업의 완료**다. 이미 생성된 n5 출력은 scoring/조립으로 이어가며 재생성하지 않는다.
+## Figure 4 — 핵심 disease-integrity 주장을 가장 먼저 보강
+**4A: 환자 단위 효과 비교 그림.** Cohort × cell type별로 `PBISC − comparator`의 logFC correlation 차이와 CI를 보여주는 forest plot을 만든다. scSemi를 주 비교로 두고 deconvolution은 지원하는 expression endpoint에서 별도로 비교한다. 5-fold와 exact-five를 분리하며, PBISC가 뒤지는 결과도 표시한다.
+**4B: 서로 다른 관점의 질병 신호.** LogFC r를 주 지표로 두는 안을 제안하며, DEG 방향 일치·CAT curve·partial correlation은 서로 다른 보조 질문으로 둔다. 최종 주/보조 지표 선택은 재분석 전에 고정한다. 저장된 scSemi five-fold CAT를 추가한다. 생성 세포 수를 생물학적 n으로 사용하지 않는다.
+**4C: 통계 단위와 정보 접근량.** 기존 patient-aware contrast/DEG 및 cross-draw donor CI pipeline을 완료한다. 같은 donor가 여러 visit/draw에 나타나는 것을 보존한다. 고정된 생성 결과를 재분석한 CI는 모델을 재학습했을 때의 모든 불확실성을 포함하지 않음을 명시한다. 각 방법의 support 수, unique donors, extra validation, gene axis, HVG 선택, test access, missing outputs를 표로 제시한다.
+**4D: 단순 bulk 신호 복사를 넘어서는가.** 이미 있는 bulk-broadcast, partial-r, representative-dependence 대조를 먼저 묶는다. 필요할 때만 support-only subtype reference profile과 예측 subtype proportions으로 만든 composition-only baseline을 추가한다. 이 추가 CPU 분석은 충분한 subtype annotation/support가 있을 때만 의미가 있다. Test scRNA로 reference를 적합하지 않고, broad cell-type normalization만으로 subtype 구성 효과를 제거했다고 주장하지 않는다.
+**완료 기준:** 모든 주장에 동일 환자·contrast·gene-axis 비교가 대응하고, cohort별 양/음의 효과 및 NA가 유지된다. 독립 생물학적 표본 간 변이를 무시하면 DEG 오류가 커질 수 있으므로 환자 단위를 우선한다([Squair et al., 2021](https://www.nature.com/articles/s41467-021-25960-2)). Giroux의 sex/severity confounding은 sex-linked gene 제거만으로 해결되지 않는다. 관측된 cohort contrast의 재현과 질환의 인과 효과를 구분한다.
+**종류:** 대부분 기존 campaign의 CPU 통계 완료와 재시각화. Sepsis deconvolution은 5개의 기존 adoption receipts를 사용하고 재실행하지 않는다. 다른 cohort의 실제 support 차이로 필요한 계산은 기존 campaign 범위에서만 남은 작업으로 센다.
+## Figure 5 — rare population이 있다는 것을 직접 확인
+**5A: matched population views.** 같은 held-out sample의 observed / PBISC / scSemi를 나란히 놓는다. Observed에서만 fit한 공통 projection을 사용하고, 전체 parent population을 회색, rare state를 강조한다. Observed-only 규칙으로 사례를 정하고 성공·과소생성·관측 음성 사례를 포함한다. Sepsis MS1-like/cycling monocyte, Imperial cycling T/NK 등은 사전 정의와 marker availability를 확인할 후보다.
+**5B: 독립 marker identity.** Rare-state 정의, 사례 선택, projection 및 annotator 입력에 사용하지 않은 유전자들로 별도의 발현/co-detection 패널을 만든다. 기존 73-marker cache는 marker-space illustration에는 충분하지만 독립적인 전사체 검증 자체는 아니다. Full 6k 출력에서 소수 유전자만 추출한다. 세 방법과 observed에서 모두 측정 가능한 gene만 사용하며, marker 역할은 생물학적 근거로 정한다.
+**5C: rare detection의 신뢰도.** Parent population 안에서 marker co-detection, abundance와 false-positive-like calls를 함께 본다. Observed zero는 유한한 sc sampling에서의 non-detection이므로 진정한 생물학적 부재로 단정하지 않는다. 동일 depth/cell budget의 nested subsampling과 draw별 일관성을 보충자료로 둔다.
+**중요한 K 구분:** 기존 paired K1000/K4000 자료의 generated-cell count는 둘 다 1,500/sample이다. 이는 recipe K 감도 분석이며 1,500 대 4,000 생성 세포 수 비교가 아니다. Actual N_gen, K_cond, depth를 따로 표기한다.
+**완료 기준:** 보기 좋은 UMAP만으로 결론 내리지 않고, 독립 marker·donor 반복·관측 음성/불일치 사례가 함께 있다. Real/generated cell의 일대일 대응을 암시하지 않는다. Bulk 조건에서 관측 rare state를 회복했다는 결론과 새 생물학적 population을 발견했다는 결론을 구분한다.
+**종류:** 기존 세포·cache를 사용하는 새 CPU 분석/그림. Strict 전체 exporter의 20-unit gate를 우회하지 않고, 먼저 완료된 검증 subset의 범위를 명시한 illustration을 만든다.
+## Figure 6 — 구체적인 기존 논문 결과 하나를 재현
+**추천 후보:** Stephenson et al. (2025), *Temporal multi-omics analysis of COVID-19 in end-stage kidney disease*, Figure 3C의 monocyte HLA-II 감소. Severe/critical과 mild/moderate COVID의 비교에서 HLA-DPB1/DPA1/DRB1/DRA/DQA1 감소가 보고돼 있다([원 논문](https://pmc.ncbi.nlm.nih.gov/articles/PMC12366655/)).
+**기존 재료:** Imperial bulk-only 생성 결과, 5개 유전자, bulk 및 clinical metadata가 모두 있다. 로컬 metadata상 164 bulk-only samples/76 donors 중 paired registry의 모든 donor를 보수적으로 제외하면 97 samples/45 donors가 남으며 모두 wave1이다. 이는 analytic cohort를 확정한 수치가 아니다. 로컬 sample-time severity 집계에서는 COVID+ 81 samples 중 70 mild/moderate, 11 severe/critical이며, 두 범주에 모두 등장하는 donor가 4명이다.
+**설계 순서:**
+1. 원 논문의 severity 정의(patient maximum인지 sample-time인지), time window, 포함/제외 조건을 원문·metadata와 대조해 고정한다. 서로 다른 contrast를 같은 재현으로 부르지 않는다.
+2. 모든 paired donor와 bulk-only target donor를 분리하고, 회복기 포함 여부·repeated visits·wave를 명시한다.
+3. Paired held-out observed cells에서 해당 marker와 monocyte localization을 먼저 점검한다.
+4. 기존 bulk-only 생성 결과에서 donor-aware HLA-II contrast를 계산하고, raw bulk의 같은 유전자 신호와 함께 보여준다. Generated cells를 독립 환자로 세지 않는다.
+5. 기존 adaptation draws/readout에 대한 민감도를 확인한다. 원 논문의 방향·세포군·시점 조건을 얼마나 재현했는지 각각 보고한다.
+**권장 패널:** cohort/adaptation 그림 → 원 논문의 재현 대상 → paired validation → bulk-only donor-level effect와 CI → raw-bulk 비교.
+**한계:** 이 HLA-II 방향은 과거 workflow에서도 살펴봤다. 기존 데이터의 분석은 **후향적 재현**이며 새 blind validation이 아니다. 독립 확인은 사용자 준비 데이터에서 별도로 정의한다. Wave1-only 집합으로 wave2 steroid-associated population을 재현하려고 하지 않는다. 기존 Ye bulk-only 집합에는 pediatric sepsis cases가 없어 소아 cycling-monocyte 재현을 우선 대상으로 삼지 않는다.
+**종류:** endpoint 정의 + 기존 출력의 CPU 재분석이 우선. 새로운 모델 학습이나 bulk-only 재생성을 기본 요구로 두지 않는다.
+## Supplementary의 역할
+1. **데이터 사용·공정성 표:** corpus/target donor overlap, support/validation/HVG/gene-axis/readout 접근 범위.
+2. **전체 성능·예외:** 모든 cohort/cell type/draw, full FT/rank trade-off, complete CAT/RBO, donor counts 및 NA. 주 그림에서 평균으로 숨겨지는 예외를 보존한다.
+3. **Readout·평가법 감도:** Pearson/Spearman, patient-aware DEG, official scSemi versus modified readout, gene-mask sensitivity. 기존 자료를 같은 비교 단위로 묶는다.
+4. **사전학습·일반화 대조:** 기존 scratch와 cross-cohort 결과를 정확한 fold/seed 범위로 표시한다. 모든 cohort에서의 독립 재현처럼 확대하지 않는다.
+5. **Rare robustness:** marker 정의·독립 검증 marker·모든 사례·depth/N_gen subsampling·zero handling. Figures 4/5의 정량/정성 역할은 유지한다.
+## 실제 새 GPU 실험이 필요할 수 있는 두 가지 조건
+- **승자/안정성 주장:** 기존 결과로 architecture 또는 total-five rank의 안정성을 주장할 수 없는데 그 주장이 필수인 경우에만, 경쟁 후보를 좁혀 matched sampling seeds 또는 adaptation-training seeds를 추가한다. 이기게 될 때까지 반복하지 않는다.
+- **환자별 conditioning 기전 주장:** 기존 SLE opposite-group/permuted-donor/cohort-mean controls는 이미 있다. Real-bulk에 adapted한 모델의 기전까지 주장해야 할 때만, 기존 adapter 하나에서 correct bulk와 사전에 정한 wrong-bulk intervention을 같은 sampling noise로 비교한다. 이는 새 추론이며 새 학습은 아니다.
+둘 다 현 시점의 기본 필수 과제가 아니다. 새 protocol이 필요할 때에는 checkpoint/support/test/validation/mask/conditioning/seed/readout 계약을 reuse registry와 대조한 뒤 바뀐 연산만 수행한다.
+## 권장 진행 순서와 종료 조건
+**1단계:** Figure1 데이터 사용 경계와 Figure4 비교 계약 확정. Figure6 논문 endpoint 선택은 병행한다.
+**2단계:** 기존 strict Figure3/4 campaign의 완료된 출력을 회수·검증·scoring한다. 기존 Figure2/rank/CAT/readout 자료는 재시각화한다. 새 campaign을 추가하지 않는다.
+**3단계:** Figure5의 두 rare-state 사례에 공통 projection + 독립 marker + 음성 사례를 붙여 작은 완성 패널을 먼저 만든다. 기존 결과로 분류가 안 되면 그 한계를 그대로 남긴다.
+**4단계:** Figure6의 한 endpoint를 기존 결과로 후향 재현한다. 새로운 독립 확인은 데이터와 질문이 고정된 뒤 별도 계획으로 둔다.
+**5단계:** 위 결과가 나온 뒤에만 조건부 GPU 항목의 필요성을 판단한다. 유의성이나 순위가 원하는 방향이라는 것을 종료 조건으로 삼지 않는다. 비교의 완결성, 불확실성, 재현 가능성이 종료 기준이다.
+## 재사용 근거 경로
+`M`은 Model8, `D`는 이 덱 폴더, `P=M/plans/paper_figures_strictpairs_20261012`, `F=M/plans/paired_finetune_intradataset_20260927`이다.
+- Corpus: `paper-release-20260928/manuscript/source_data/corpus/{README.md,corpus_summary.json}`; `M/runs/f01_6k_1x_seed20260627/ALLCELL_DATA_CONTRACT.json`.
+- SLE: `P/source_data/ablation_pretrain_{pc,composition_summary}.csv` 및 해당 receipts; `D/data/sec03_panel_seeds.csv`.
+- Healthy: `D/data/sec07_ft_modes_accuracy.csv`, `P/source_data/fig2_historical_rank_full.csv`, `P/protocol/splits/immicro/healthy/`, `P/generated/immicro/healthy/`, `P/scores/immicro/healthy/`.
+- Disease: `P/tools/{donor_statistics.py,disease_signal.py,finalize_outputs.py,deconv_strict.py}`, completed-unit `statistics/`, `F/scores_contrast/`, `P/deconvolution/ye2026_sepsis/disease/drawN/ADOPTION.json`.
+- Readout: `F/scsemi/analysis/output_variants_20261003/per_draw.csv`; `F/scores_nosex/giroux2022/`.
+- Rare: `M/plans/rare_states_paired_20261008/work/`; `F/data/<cohort>/`, `F/scoring_cache/<cohort>/observed_labels/`; `P/tools/{rare_cache_builder.py,export_rare_visuals.py}`.
+- Bulk-only: `M/plans/bulkonly_lora5_20261006/analysis/features/imperial_eskd/meta.csv`; `.../work/imperial_eskd/{bulkonly,paired_gen}/drawN/{pb_l1.npz,pb_l2.npz,cells.parquet,RECEIPT.json}`; corresponding `bulkonly_lora5_k4000_20261006` outputs.
+- Reuse registry: `M/EXPERIMENT_INDEX.md` and `M/EXPERIMENT_REUSE_INDEX.jsonl`.

	@@ -0,0 +1,47 @@

+# Figure 1–3 확장본: 140장
+> 현재 작업 덱은 [142장 전체 cell-type·최종 팔레트 버전](../celltypes_palette_master_20261013/README.md)이다. 이 문서는 보존된 140장 버전의 기록이다.
+현재 파일: [PPTX](../../PBISC-Diffusion_expanded_master_20261013_enriched.pptx) · [PDF](../../PBISC-Diffusion_expanded_master_20261013_enriched.pdf)
+사용자가 지정한 Figure 1–6 구분을 유지하고, 재배치된 131장 덱을 140장으로 확장했다. 이전 131개 페이지의 ID와 원본 76개 ID는 각각 한 번씩 남아 있다. 기존 파일과 원래 scientific figure assets는 그대로 보존했다.
+| 현재 페이지 | 변경 내용 |
+|---|---|
+| 10 | Figure 1: 실제 저장된 0/17/33/50 단계의 PCA1/2를 네 패널로 표시 |
+| 13 | Figure 2: context와 trunk가 각각 어디이며 무엇이 달라지는지 보여주는 도식 |
+| 14 | Pred/oracle hint의 출처, condition encoder 앞의 주입 위치, 같은 denoiser를 쓰는 비교 |
+| 15 | Architecture validation loss 전체 및 후반 진행. 이전 supplementary loss 페이지를 갱신해 이동 |
+| 16–17 | SLE architecture benchmark와 정확한 width/parameter/completion 표 |
+| 18–20 | Gene-context benchmark, validation 진행, 실제 gene 수와 parameter 표 |
+| 21 | 모든 10개 학습 arm의 parameter, 기록된 update당 시간 및 누적 학습시간 |
+| 22–23 | Real-cell repeat의 의미와 새 이름을 반영한 공통 축 ablation |
+| 26–27 | Figure 3: bulk와 pseudobulk expression 값의 직접 비교 및 donor별 오차 |
+Figure 범위: F1 5–10, F2 11–23, F3 24–33, F4 34–39, F5 40–41, F6 42–44. Supplementary는 45쪽부터다.
+## 도식과 수치의 의미
+- **Context:** bulk/depth와 measurement features를 context width C로 인코딩하고 time encoding과 혼합한 뒤, 일곱 trunk block에 각 projection을 통해 더한다. Context ×4는 C=512→2,048이다.
+- **Trunk:** denoiser의 hidden block widths다. Trunk ×4는 down widths [1,024,1,024,512,256]을 [4,096,4,096,2,048,1,024]로 늘리고 context C는 512를 유지한다. Width 배수와 parameter 배수는 다르다.
+- **Pred/oracle hint:** 같은 hint-trained denoiser에서 9개 cell-type fractions만 교체한다. `[base G+6 | hint 9 | measurements 2G]`가 condition encoder에 들어간다. Pred는 별도 MLP의 예측, oracle은 target donor의 관측 scANVI fractions를 사용한다. Predictor는 3.206M parameters를 추가하며 별도 시간 기록은 없다.
+- **Real-cell repeat:** 같은 42 donors에서 donor당 100개씩 겹치지 않는 두 실제 세포 묶음을 비교한다. 한 묶음은 reference, 다른 묶음은 평가 대상이다. 새로운 donor 집합이나 이론적 최고 성능을 뜻하지 않는다. 본문과 보충 그림의 Real R1/R2 약어도 고쳤다.
+## 검증 범위와 표시한 한계
+1. **Trajectory:** 웹사이트용 저장 좌표와 point ID를 사용한다. 기존 ctx2048 E12 EMA의 한 donor pseudobulk illustration이다. 최종 모델의 paired 검증 결과로 부르지 않는다. 4,096개 점과 4,362개 실제 reference, 고정 PCA axes를 유지했다. 최종 cell-type 색을 단계 진행률에 따라 드러내며, timestep confidence로 해석하지 않는다. [참고 웹사이트](https://www.virtualhumanlab.com/research/pbisc-diffusion/)
+2. **Validation:** 내부 corpus validation recipe의 loss이며 SLE biological benchmark와 별도다. 전체 저장된 146개 epoch 기록을 기존 CSV와 대조했다. 16× 모델은 E2 이후 중단돼 최종 loss와 saturation을 NA로 표시한다. 1% endpoint rule은 경험적 종료값 기준이며 수학적 수렴 증명이 아니다.
+3. **Time:** history의 `train_seconds`와 optimizer updates로 계산했다. Loader와 host 조건 변화가 포함된 실제 관측 시간이며 통제된 hardware benchmark가 아니다. Resume 뒤 reset될 수 있는 RUN_STATUS wall_seconds를 총시간으로 사용하지 않았다.
+4. **Mismatch:** 건강인 107 donors × 최종 모델의 공통 측정 5,508 genes. 같은 gene axis에서 `ln(1 + 10^6 × count / gene-axis total)`로 비교했다. 모든 측정 zero를 유지했다. 기존 10-pair other-pool calibration을 불러왔고 새로 fit하지 않았다. Paired calibration은 diagnostic이며 strict-five adaptation 결과와 다르다.
+5. **수치:** 새 mismatch 도표는 기존 assays와 calibrated conditions의 binning 및 donor별 기술통계다. 나머지는 기존 로그·score·좌표의 재시각화다. 새 training/inference/projection fit은 실행하지 않았다.
+## 재현과 페이지 연결
+- [현재 페이지 ↔ 이전 재배치 페이지 ↔ 원래 131장/76장 번호](page_map.csv)
+- [Source manifest](manifest.json)
+- [검수 결과](qa/VERIFICATION.json)
+- Architecture: `arch_result.json`, `arch_training_arms.csv`, `arch_loss_and_timing_by_part.csv`, `arch_inference_variant_register.csv`.
+- Trajectory: `trajectory_provenance.json`, owned source snapshot와 좌표 CSV.
+- Mismatch: `mismatch_result.json`, donor별 CSV와 density-bin CSV.
+- Labels: `labels_result.json`, source-bound plot proofs.
+- `render.py`는 저장된 enrichment assets를 읽어 덱을 조립하고 PDF로 변환한 다음 검증한다. Host guard/CPU memory limits를 적용한 user-systemd unit에서 실행한다. 과거 builder를 실행해 현재 순서를 덮어쓰지 않는다.

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