Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/locoL_lora64sat_s69250_K1000/summary.json

@@ -0,0 +1,531 @@
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+ "seconds": 8.6
+}

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/xcalL_K1000/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: giroux2022, 24 scored donors
+
+Arms: xcalL_K1000.  Every arm is scored on the same donors against the same observed reference.
+
+## Inputs and fixed contracts
+- Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/reference.npz` (sha dd0152f7bde4), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022;
+  observed cells sum exactly to the features pseudobulk of every donor. Observed labels: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/observed_labels/labels/labels.parquet.
+- Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
+  (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
+  depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
+- Labels: frozen f01common scANVI (`predict_scanvi.py`, CPU, zero query epochs), six-type gate ['T cell', 'B cell', 'Monocyte', 'NK cell', 'DC', 'Other']; query genes =
+  the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1006 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5563 genes).
+
+## Definitions
+1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
+   `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
+   donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
+   fraction of donors whose generated composition is closest (L1) to their own observed composition.
+2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
+   genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
+   observed pseudobulk among the 24 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
+3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
+   (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
+   Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
+   permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
+   n = min(150, n_cells // 2) (3600 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
+   without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
+   (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
+   Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
+   their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
+   perfect-generator control: precision 0.9981, coverage 1.0000.
+   **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
+   genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
+   un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
+   0.9981, coverage 1.0000.
+4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
+   Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
+5. **Group contrast** (T, B, Monocyte, NK): case groups ['severe'] (n = 11) vs control groups ['mild'] (n = 13); rule: explicit --case-groups / --control-groups; groups from features groups.
+   Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
+   cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
+   cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
+   logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
+   statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
+   evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
+   the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
+   observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
+   CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
+   generated logFC on the bulk logFC (residual_specificity.py).
+6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): the SLE benchmark's score (`total_top50_overlap` of
+   `bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, Welch backend: the "top-50 DEG 81 / 84" of the final model), run
+   with the evaluator's own functions on the same donor x type pseudobulks as the contrast. Per cell type, observed and
+   generated separately: donor x type summed counts on all 5563 scoring genes (donors of the two groups with
+   >= 1 cell of the type) -> `welch_bh_deg` (log1p CPM, two-sided Welch t-test case vs control, Benjamini-Hochberg over the
+   genes with a defined p-value; needs >= 2 donors per group) -> `top_deg_genes` (FDR < 0.05, descending |logFC|,
+   first 50; shorter when fewer genes pass). `top50_overlap` = |observed list & generated list|; `top50_jaccard`
+   the evaluator's Jaccard. Summary `sum` = total over the scored types (the quantity reported as 81), `max` = the sum of
+   the observed list lengths (the "of 262"), `sum_4type` = T + B + Monocyte + NK. A type is scored when its observed test
+   is defined; an arm whose generated test is undefined for a scored type gets overlap 0. Types: T cell, B cell, Monocyte, NK cell, DC
+   (aggregates: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/deg_reference.npz).
+   **Broadcast analogue** (`top50_overlap_broadcast`, as `sle_broadcast.py` `broadcast_input_bulk`; not part of the
+   published benchmark): the same test on the arm's condition bulk_counts (scoring genes, the 11 + 13
+   donors), one whole-PBMC top-50 list, compared with every type's observed list.
+
+## Files
+- `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
+- `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
+  Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
+- `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
+  top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
+- `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
+  UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
+  top-50 gene lists, Ensembl ids, rank order).

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/xcalL_K1000/contrast.csv

@@ -0,0 +1,14 @@
+arm,celltype,statistic,n_genes,r_generated,r_broadcast,partial_r_generated_given_broadcast,r_generated_vs_broadcast,n_case_donors_obs,n_control_donors_obs,n_case_donors_gen,n_control_donors_gen,n_cells_obs,n_cells_gen,deg_scored,top50_overlap,top50_jaccard,obs_top_list_size,gen_top_list_size,n_sig_obs,n_sig_gen,top50_overlap_broadcast,top50_jaccard_broadcast,broadcast_top_list_size,n_sig_broadcast
+xcalL_K1000,T cell,mean_cp10k,2908,0.46807968377476183,0.663884796401021,0.1378394024331679,0.578399935965255,11,13,11,13,145034,22683,,,,,,,,,,,
+xcalL_K1000,T cell,pseudobulk_log1p_cpm,2908,0.5781521360920436,0.8229501008768898,0.174416270575966,0.6068337956615151,11,13,11,13,145034,22683,,,,,,,,,,,
+xcalL_K1000,B cell,mean_cp10k,3053,0.18489080648085457,0.5645155961562724,0.0642267115185676,0.23626910571239182,11,13,11,13,17383,1743,,,,,,,,,,,
+xcalL_K1000,B cell,pseudobulk_log1p_cpm,3053,0.2293292420424647,0.7209755672221485,0.05554884466742807,0.2666241778418804,11,13,11,13,17383,1743,,,,,,,,,,,
+xcalL_K1000,Monocyte,mean_cp10k,3056,0.29373332218950676,0.6980894090417796,0.1138857039889066,0.3097011856278731,11,13,11,13,47311,6880,,,,,,,,,,,
+xcalL_K1000,Monocyte,pseudobulk_log1p_cpm,3056,0.3838923902659595,0.8425716586211682,0.08994132248074382,0.4030034433605325,11,13,11,13,47311,6880,,,,,,,,,,,
+xcalL_K1000,NK cell,mean_cp10k,2955,0.3319716204618838,0.6215413769335235,0.16384350896165084,0.3398993588786841,11,13,11,13,29061,3779,,,,,,,,,,,
+xcalL_K1000,NK cell,pseudobulk_log1p_cpm,2955,0.4032336075022692,0.767238494858386,0.20685286722172372,0.36454851808197813,11,13,11,13,29061,3779,,,,,,,,,,,
+xcalL_K1000,T cell,welch_top50_deg,5563,,,,,11,13,11,13,145034,22683,True,7,0.1346153846153846,50,9,306,9,9,0.16071428571428573,15,15
+xcalL_K1000,B cell,welch_top50_deg,5563,,,,,11,13,11,13,17383,1743,True,4,0.4444444444444444,9,4,9,4,7,0.4117647058823529,15,15
+xcalL_K1000,Monocyte,welch_top50_deg,5563,,,,,11,13,11,13,47311,6880,True,4,0.5714285714285714,7,4,7,4,7,0.4666666666666667,15,15
+xcalL_K1000,NK cell,welch_top50_deg,5563,,,,,11,13,11,13,29061,3779,True,5,0.09090909090909091,50,10,83,10,8,0.14035087719298245,15,15
+xcalL_K1000,DC,welch_top50_deg,5563,,,,,11,13,11,13,4328,536,True,1,0.14285714285714285,7,1,7,1,5,0.29411764705882354,15,15

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/xcalL_K1000/per_donor.csv

@@ -0,0 +1,25 @@
+arm,donor,group,n_generated_cells,n_observed_cells,n_reference_cells,gen_T cell,obs_T cell,gen_B cell,obs_B cell,gen_Monocyte,obs_Monocyte,gen_NK cell,obs_NK cell,gen_DC,obs_DC,gen_Other,obs_Other,comp_l1,comp_l1_obs_loo_null,pb_r,pb_own_rank,pb_top1,precision,coverage,precision_depth_matched,coverage_depth_matched,mono_pct_gen,mono_pct_obs,median_umi_gen,median_umi_obs
+xcalL_K1000,450905_d0,mild,1500,5914,150,0.37466666666666665,0.30943523841731485,0.018666666666666668,0.036185322962461956,0.49933333333333335,0.4293202570172472,0.08133333333333333,0.12089956036523504,0.018,0.009976327358809604,0.008,0.09418329387893135,0.2865363544132567,0.6266504923198378,0.8838614999813391,2,False,0.98,0.7933333333333333,0.98,0.8266666666666667,49.93333333333334,42.93202570172472,801.0,574.5
+xcalL_K1000,450905_d14,mild,1500,7407,150,0.548,0.4422843256379101,0.030666666666666665,0.02929661131362225,0.23533333333333334,0.27420008100445525,0.152,0.15782368030241664,0.016,0.02983664101525584,0.018,0.06655866072633995,0.21417145943026872,0.36155260046011806,0.8849719125896831,2,False,1.0,0.9533333333333334,0.9933333333333333,0.9333333333333333,23.533333333333335,27.420008100445525,701.0,558.0
+xcalL_K1000,0B943B_d0,mild,1500,11344,150,0.6093333333333333,0.6220028208744711,0.04066666666666666,0.08312764456981664,0.22533333333333333,0.12588152327221439,0.09266666666666666,0.11865303244005641,0.02,0.023977433004231313,0.012,0.026357545839210156,0.19890362012223797,0.1791278384873501,0.8770239576190935,13,False,0.9866666666666667,0.94,0.98,0.9333333333333333,22.53333333333333,12.588152327221438,681.0,539.0
+xcalL_K1000,0B943B_d14,mild,1500,15650,150,0.564,0.5652396166134185,0.06533333333333333,0.11175718849840256,0.18933333333333333,0.17086261980830672,0.14266666666666666,0.09789137380191694,0.007333333333333333,0.009968051118210863,0.03133333333333333,0.04428115015974441,0.12649201277955271,0.10063602424453227,0.878288664070329,1,True,1.0,0.9333333333333333,1.0,0.92,18.933333333333334,17.08626198083067,637.5,461.0
+xcalL_K1000,7085CA_d0,mild,1500,12585,150,0.30666666666666664,0.21660707191100517,0.018,0.024791418355184745,0.5946666666666667,0.5874453714739769,0.06533333333333333,0.13198251887167262,0.012666666666666666,0.017719507350019866,0.0026666666666666666,0.021454112038140644,0.19456177989670248,0.8829287440863147,0.8921118605535212,1,True,0.98,0.9133333333333333,0.9733333333333334,0.94,59.46666666666667,58.74453714739769,898.0,640.0
+xcalL_K1000,7085CA_d7,mild,1500,9515,150,0.606,0.521492380451918,0.034,0.0722017866526537,0.124,0.10110352075669994,0.20933333333333334,0.2725170782974251,0.019333333333333334,0.019863373620599055,0.007333333333333333,0.012821860220704152,0.21480819758276398,0.3632282731474772,0.8857277701328309,5,False,0.9933333333333333,0.94,0.9733333333333334,0.9266666666666666,12.4,10.110352075669994,695.0,502.0
+xcalL_K1000,7085CA_d14,mild,1500,11887,150,0.6233333333333333,0.472533019264743,0.046,0.07133843694792631,0.12933333333333333,0.16210986792294102,0.18,0.24270211155043325,0.016666666666666666,0.018591738874400604,0.004666666666666667,0.032724825439555814,0.3016006281371806,0.2993283707516418,0.8912388016794238,1,True,0.98,0.9266666666666666,0.9866666666666667,0.9266666666666666,12.933333333333334,16.2109867922941,703.0,536.0
+xcalL_K1000,2AD75E_d0,mild,1500,7693,150,0.7093333333333334,0.6034056934875861,0.05466666666666667,0.06408423241908229,0.11066666666666666,0.14064734173924348,0.09933333333333333,0.15949564539191471,0.007333333333333333,0.025347718705316522,0.018666666666666668,0.007019368256856883,0.23514987651111408,0.20681142084856263,0.8582831707179684,23,False,1.0,0.8333333333333334,0.9866666666666667,0.7933333333333333,11.066666666666666,14.064734173924348,521.0,532.0
+xcalL_K1000,2AD75E_d7,mild,1500,10499,150,0.6246666666666667,0.5808172206876845,0.13933333333333334,0.14372797409277074,0.12066666666666667,0.10277169254214687,0.08866666666666667,0.14715687208305553,0.005333333333333333,0.013715591961139155,0.021333333333333333,0.011810648633203162,0.1425342096072643,0.27874882058987843,0.8384550760646026,19,False,0.9933333333333333,0.7266666666666667,0.9933333333333333,0.7666666666666667,12.066666666666666,10.277169254214687,399.0,443.0
+xcalL_K1000,2AD75E_d14,mild,1500,13692,150,0.656,0.5811422728600643,0.07066666666666667,0.12503651767455448,0.162,0.14198071866783524,0.08333333333333333,0.12496348232544552,0.018666666666666668,0.016579024247735902,0.009333333333333334,0.010297984224364592,0.19392930178206258,0.19762923258298543,0.8818531265012808,16,False,1.0,0.7733333333333333,0.9866666666666667,0.7466666666666667,16.2,14.198071866783524,726.0,428.0
+xcalL_K1000,B85D75_d0,severe,1500,11441,150,0.8166666666666667,0.7173324010138974,0.035333333333333335,0.031203566121842496,0.06,0.11441307578008915,0.08,0.06039681845992483,0.006,0.016519535005681322,0.002,0.06013460361856481,0.24613442880867054,0.35882358586377083,0.8861056466368491,4,False,0.9933333333333333,0.9666666666666667,0.9933333333333333,0.9733333333333334,6.0,11.441307578008916,743.5,347.0
+xcalL_K1000,B85D75_d7,severe,1500,12328,150,0.734,0.6435756002595717,0.05466666666666667,0.06448734587929916,0.112,0.13741077222582737,0.08,0.04201817001946788,0.012,0.027741726151849447,0.007333333333333333,0.08476638546398442,0.25681245944192077,0.28301793033829503,0.8906212485999769,2,False,0.9866666666666667,0.8666666666666667,0.9866666666666667,0.8533333333333334,11.200000000000001,13.741077222582737,747.5,363.0
+xcalL_K1000,180E1A_d0,severe,1500,7157,150,0.7273333333333334,0.7019700992035769,0.038,0.04135811094033813,0.12266666666666666,0.13175911694844208,0.082,0.0655302501047925,0.018,0.013273718038284197,0.012,0.04610870476456616,0.09311853197335951,0.31372800769589615,0.8819728772547242,11,False,0.9933333333333333,0.98,1.0,0.9466666666666667,12.266666666666666,13.175911694844208,652.0,433.0
+xcalL_K1000,180E1A_d7,severe,1500,10591,150,0.792,0.7348692285903126,0.029333333333333333,0.04909829100179398,0.07866666666666666,0.09064299877254273,0.08666666666666667,0.08280615617033331,0.006,0.010480596733075253,0.007333333333333333,0.03210272873194221,0.12198256381204169,0.37628768415060193,0.8870638123470298,5,False,0.9866666666666667,0.9466666666666667,0.9866666666666667,0.9466666666666667,7.866666666666666,9.064299877254273,692.0,408.0
+xcalL_K1000,180E1A_d14,severe,1500,12511,150,0.682,0.680840860043162,0.042,0.05147470226200943,0.14933333333333335,0.12277196067460634,0.104,0.0800095915594277,0.018666666666666668,0.009831348413396212,0.004,0.05507153704739829,0.1210924786188155,0.28939519394073066,0.887183782641724,7,False,0.9933333333333333,0.9466666666666667,0.9866666666666667,0.94,14.933333333333335,12.277196067460634,724.0,342.0
+xcalL_K1000,82CCF5_d0,severe,1500,10125,150,0.6093333333333333,0.5422222222222223,0.10933333333333334,0.11832098765432099,0.172,0.1539753086419753,0.08466666666666667,0.09303703703703704,0.008666666666666666,0.016691358024691356,0.016,0.07575308641975309,0.17027160493827148,0.18419072822032903,0.8792446361988654,5,False,0.9933333333333333,0.9,0.9933333333333333,0.88,17.2,15.397530864197531,663.0,390.0
+xcalL_K1000,82CCF5_d7,severe,1500,10860,150,0.6493333333333333,0.619060773480663,0.068,0.09972375690607735,0.144,0.14152854511970533,0.11466666666666667,0.09410681399631676,0.019333333333333334,0.013627992633517496,0.004666666666666667,0.03195211786372008,0.11801841620626144,0.19419086678828923,0.8937154637642464,1,True,0.98,0.9333333333333333,0.9466666666666667,0.9266666666666666,14.399999999999999,14.152854511970533,742.0,497.0
+xcalL_K1000,82CCF5_d14,severe,1500,11056,150,0.66,0.6330499276410999,0.062,0.10121201157742403,0.14933333333333335,0.11369392185238784,0.09333333333333334,0.06828871201157742,0.022,0.014562228654124458,0.013333333333333334,0.0691931982633864,0.19014375301495418,0.2787071044352101,0.8890382173141004,4,False,0.9933333333333333,0.94,0.9666666666666667,0.9466666666666667,14.933333333333335,11.369392185238784,752.0,394.0
+xcalL_K1000,BAAF62_d0,mild,1500,14741,150,0.6033333333333334,0.5279153381724442,0.03933333333333333,0.0384641476154942,0.13733333333333334,0.1854690997897022,0.18866666666666668,0.1717658232141646,0.02,0.023132758971575875,0.011333333333333334,0.053252832236618954,0.18637604866246077,0.14602913946264867,0.8878131333165277,6,False,0.9933333333333333,0.9333333333333333,0.98,0.94,13.733333333333334,18.54690997897022,671.0,449.0
+xcalL_K1000,BAAF62_d7,mild,1500,7297,150,0.726,0.6932986158695355,0.04466666666666667,0.04070165821570509,0.114,0.13389063998903658,0.09333333333333334,0.0941482801151158,0.017333333333333333,0.016033986569823216,0.004666666666666667,0.021926819240783883,0.07593147868987249,0.25991971231178185,0.8862331179345716,9,False,1.0,0.9333333333333333,0.9733333333333334,0.9533333333333334,11.4,13.389063998903659,708.0,580.0
+xcalL_K1000,BAAF62_d14,mild,1500,11923,150,0.7166666666666667,0.5431518913025245,0.02,0.019206575526293717,0.12866666666666668,0.2843244149962258,0.10933333333333334,0.10416841398976767,0.02266666666666667,0.032793759959741674,0.0026666666666666666,0.016354944225446616,0.35894623836282813,0.2201331132920326,0.8946841447291194,1,True,0.98,0.8533333333333334,0.9666666666666667,0.8466666666666667,12.866666666666667,28.43244149962258,719.0,513.0
+xcalL_K1000,0BF51C_d0,severe,1500,6510,150,0.2853333333333333,0.4012288786482335,0.013333333333333334,0.01674347158218126,0.5713333333333334,0.4609831029185868,0.09533333333333334,0.07726574500768049,0.020666666666666667,0.006144393241167435,0.014,0.03763440860215054,0.28588018433179724,0.5556611725994968,0.8786563254317361,1,True,0.9133333333333333,0.68,0.8866666666666667,0.6466666666666666,57.13333333333333,46.09831029185868,862.0,628.0
+xcalL_K1000,0BF51C_d7,severe,1500,10105,150,0.7366666666666667,0.612370113805047,0.04,0.05700148441365661,0.14466666666666667,0.1607125185551707,0.05266666666666667,0.05294408708560119,0.012,0.0073231073725878275,0.014,0.10964868876793667,0.25794689097806367,0.22247826940326798,0.8748341873724111,17,False,1.0,0.8866666666666667,0.9933333333333333,0.9066666666666666,14.466666666666667,16.071251855517072,699.0,509.0
+xcalL_K1000,0BF51C_d14,severe,1500,11419,150,0.7613333333333333,0.6330676941938874,0.048,0.07960416849111131,0.112,0.151589456169542,0.06,0.07907872843506436,0.012666666666666666,0.010508801120938787,0.006,0.04615115158945617,0.26084700937034766,0.1611535225979176,0.8831859310767797,5,False,0.9933333333333333,0.8866666666666667,0.9866666666666667,0.9066666666666666,11.200000000000001,15.1589456169542,700.0,541.0