Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/tools/build_archive_request.py

@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+"""Archive-maintenance request for the paired fine-tuning benchmark (PBISC-Diffusion paper, pbisc-diffusion).
+
+Follows paper-release-20260928/maintenance/POLICY.md: every payload is listed explicitly (host, exact path, kind,
+state); generated arrays are not payloads but inference locations with their receipts; work that is still running
+(the scSemiProfiler baseline) is registered as running/planned locations only.
+
+Payloads (completed): code, documents, figures and their source CSVs, score tables, paired-data manifests and
+small data (features, pairs, folds, splits, cell metadata), the condition NPZs every sampler read, run receipts
+(RUN.json / history.json, which carry every fine-tuned checkpoint's sha256), queues, and the recommended-protocol
+LoRA checkpoints (all-training-donor LoRA per fold, and the 5-pair LoRA per draw) on both hosts.
+Not uploaded (location/provenance only): other fine-tuned checkpoints (sha256 in their RUN.json), the per-cell
+count arrays cells_panel6k.npz / cells_full.npz (sha256 in BUILD.json / MANIFEST.json; rebuilt from the datalake
+processing outputs), generated NPZs.
+usage: build_archive_request.py OUT.json
+"""
+from __future__ import annotations
+
+import glob
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+T = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927")
+DATASETS = ("immicro", "ye2026_sepsis", "imperial_eskd", "giroux2022", "ji2025_chili")
+
+
+def files(patterns, root=T):
+    out = []
+    for p in patterns:
+        for f in sorted(glob.glob(str(root / p), recursive=True)):
+            q = Path(f)
+            if q.is_file() and not q.is_symlink() and "__pycache__" not in q.parts and not q.name.endswith((".bak", ".pyc")) \
+                    and ".bak_" not in q.name:
+                out.append(str(q))
+    return sorted(set(out))
+
+
+def remote_files(patterns):
+    cmd = "cd %s && for p in %s; do ls -1d $p 2>/dev/null; done; true" % (T, " ".join(patterns))
+    res = subprocess.run(["ssh", "-n", "spark-0110", cmd], capture_output=True, text=True, check=True)
+    return sorted({str(T / l.strip()) for l in res.stdout.splitlines() if l.strip()})
+
+
+def remote_json(path):
+    res = subprocess.run(["ssh", "-n", "spark-0110", f"cat '{path}'"], capture_output=True, text=True)
+    return json.loads(res.stdout) if res.returncode == 0 and res.stdout.strip() else None
+
+
+def main(out: str) -> int:
+    assets = []
+
+    def add(paths, kind, host="server_1", state="completed"):
+        for p in paths:
+            assets.append({"host": host, "source": p, "kind": kind, "state": state})
+
+    add(files(["tools/*.py", "tools/*.sh", "tests/*.py", "figures/make_figures.py",
+               "scsemi/tools/*.py", "scsemi/tools/*.sh", "scsemi/tests/*.py"]), "code")
+    add(files(["README.md", "FEASIBILITY.md", "figures/FIGURES.md", "scsemi/README.md",
+               "data/*/QC_REPORT.md"]), "document")
+    add(files(["figures/*.png", "figures/*.pdf"]), "figure")
+    add(files(["figures/source_data/**/*.csv"]), "table")
+    add(files(["scores/*/*/summary.json", "scores/*/*/per_donor.csv", "scores/*/*/contrast.csv", "scores/*/*/README.md",
+               "scores/*/fewshot_n5/*/summary.json", "scores/*/fewshot_n5/*/per_donor.csv",
+               "scores/*/fewshot_n5/*/contrast.csv", "scores/*/fewshot_n5/*/README.md",
+               "scores/*/AGGREGATE.md", "scores/*/aggregate.json",
+               "scores/*/fewshot_n5/AGGREGATE.md", "scores/*/fewshot_n5/aggregate.json"]), "table")
+    # 2026-10-01: Giroux sex-linked-gene rescoring (scores_nosex, chrY-only sensitivity scores_nochrY) and its gene list
+    add(files([f"{d}/{p}" for d in ("scores_nosex", "scores_nochrY")
+               for p in ("README.md", "**/summary.json", "**/per_donor.csv", "**/contrast.csv", "**/README.md")]), "table")
+    add(files(["data/sex_linked_genes/*.csv", "data/sex_linked_genes/*.md",  # BUILD.json is caught by data/*/BUILD.json
+               "data/sex_linked_genes/sources/*.txt", "data/sex_linked_genes/sources/README*"]), "metadata")
+    # 2026-10-01 LoRA rank sweep (r4/r64) and equal-pair-budget calibration: per-fold and pooled scores + comparison tables
+    add(files([f"scores/*/{d}/*/{p}" for d in ("lora_rank_20261001", "cal_budget_20261001")
+               for p in ("summary.json", "per_donor.csv", "contrast.csv", "README.md")]), "table")
+    add(files(["scores/*/rankcal_20261001.md", "scores/*/rankcal_20261001.json"]), "table")
+    # equal-pair-budget calibration (alpha fitted on each lora_n<n> run's own pairs)
+    add(files(["data/*/fold*/alpha_n*.npy"]), "data")
+    add(files(["data/*/CAL_BUDGET.json"]), "metadata")
+    add(files(["data/*/features.npz", "data/*/cells_obs.parquet", "data/*/fold*/alpha.npy",
+               "data/*/fewshot_n5/draw*/alpha.npy"]), "data")
+    add(files(["data/*/pairs.csv", "data/*/folds.csv", "data/*/samples.csv", "data/*/identity_*.csv",
+               "data/*/pairs_sensitivity_offday.csv", "data/*/MANIFEST.json", "data/*/PREPARE.json",
+               "data/*/BUILD.json", "data/*/cells_full.BUILD.json", "data/*/fewshot_n5/DRAWS.json",
+               "data/*/fewshot_n5/draw*/*.txt"]), "metadata")
+    add(files(["data/*/conditions_*.npz", "data/*/fold*/*.npz", "data/*/fewshot_n5/*.npz",
+               "data/*/fewshot_n5/draw*/*.npz"]), "condition")
+    add(files(["runs/**/RUN.json", "runs/**/history.json"]), "log")
+    # server_2 ran its own copies of the scSemiProfiler queues (some exist only there): keep them as the as-run record
+    subprocess.run(["rsync", "-a", f"spark-0110:{T}/scsemi/queues/", f"{T}/scsemi/queues/as_run_server2/",
+                    "--include=server2_*.txt", "--exclude=*"], check=True)
+    add(files(["queues/*.txt", "scsemi/queues/*.txt", "scsemi/queues/as_run_server2/*.txt",
+               "scsemi/vendor/compatibility.patch",
+               "scsemi/vendor/source_manifest.json"]), "config")
+    # recommended protocol weights (LoRA merged into the base weights): server_1 datasets here, Imperial on server_2
+    add(files(["runs/*/fold*/real_lora/checkpoint_ft.pt", "runs/*/fewshot_n5/draw*/lora/checkpoint_ft.pt"]), "checkpoint")
+    add(remote_files(["runs/imperial_eskd/fold*/real_lora/checkpoint_ft.pt",
+                      "runs/imperial_eskd/fewshot_n5/draw*/lora/checkpoint_ft.pt"]), "checkpoint", host="server_2")
+    add(remote_files(["runs/imperial_eskd/*/*/RUN.json", "runs/imperial_eskd/*/*/history.json",
+                      "runs/imperial_eskd/fewshot_n5/*/*/RUN.json", "runs/imperial_eskd/fewshot_n5/*/*/history.json"]),
+        "log", host="server_2")
+
+    # ---- inference locations (not payloads) ------------------------------------------------------------------------
+    locs = []
+
+    def receipt_fields(rec: dict | None) -> dict:
+        if not rec:
+            return {}
+        keep = {}
+        for k in ("output_sha256", "checkpoint", "checkpoint_sha256", "input_condition_path", "input_condition_sha256",
+                  "condition_npz", "condition_sha256", "seed", "steps", "generated_counts_rows"):
+            if k in rec and not isinstance(rec[k], (dict, list)):
+                keep[k] = rec[k]
+        if "output_sha256" in keep:
+            keep["sha256"] = keep["output_sha256"]
+        return keep
+
+    def loc(host, npz, experiment, cohort, rec=None, state="completed"):
+        rpath = npz[:-4] + ".log"
+        item = {"host": host, "path": npz, "state": state, "experiment": experiment, "cohort": cohort,
+                "artifact_role": "inference_output"}
+        if state in ("completed", "final"):
+            item["receipt"] = rpath
+        item.update(receipt_fields(rec))
+        locs.append(item)
+
+    for ds in DATASETS:
+        # present on server_1
+        for npz in files([f"generated/{ds}/**/*.npz"]):
+            rel = Path(npz).relative_to(T / "generated" / ds)
+            rp = Path(npz[:-4] + ".log")
+            rec = json.loads(rp.read_text()) if rp.is_file() and rp.stat().st_size else None
+            if rec is None:
+                continue
+            loc("server_1", npz, f"paired_finetune_{rel.parts[0]}", ds, rec)
+        # moved to server_2 (sha-verified move; .moved marker kept on server_1)
+        for mk in files([f"generated/{ds}/**/*.moved"]):
+            npz = mk[:-6] + ".npz"
+            rp = Path(mk[:-6] + ".log")
+            rec = json.loads(rp.read_text()) if rp.is_file() and rp.stat().st_size else None
+            rel = Path(npz).relative_to(T / "generated" / ds)
+            item_state = "completed" if rec else "presence_only"
+            loc("server_2", npz, f"paired_finetune_{rel.parts[0]}", ds, rec, state=item_state)
+            locs[-1]["move_marker_server_1"] = mk
+    # Imperial outputs live on server_2
+    for npz in remote_files(["generated/imperial_eskd/*/*.npz", "generated/imperial_eskd/fewshot_n5/*/*.npz",
+                             "generated/imperial_eskd/pooled/*.npz"]):
+        rec = remote_json(npz[:-4] + ".log")
+        if rec is None:
+            continue
+        rel = Path(npz).relative_to(T / "generated" / "imperial_eskd")
+        loc("server_2", npz, f"paired_finetune_{rel.parts[0]}", "imperial_eskd", rec)
+    # scSemiProfiler draws of the other datasets that ran on server_2 (queues server2_*.txt)
+    for npz in remote_files([f"generated/{ds}/fewshot_n5/draw*/scsemi5__raw.npz" for ds in DATASETS
+                             if ds != "imperial_eskd"]):
+        rec = remote_json(npz[:-4] + ".log")
+        if rec is None:
+            continue
+        rel = Path(npz).relative_to(T / "generated")
+        loc("server_2", npz, "paired_finetune_fewshot_n5", rel.parts[0], rec)
+    # scSemiProfiler baseline still running: register in-progress / planned draws by their future output path
+    done = {l["path"] for l in locs}
+    run_queues = [T / "scsemi" / "queues" / "server1_fewshot_d0first.txt"] + \
+        sorted((T / "scsemi" / "queues" / "as_run_server2").glob("server2_*.txt"))
+    for qf in run_queues:
+        host = "server_1" if qf.name.startswith("server1_") else "server_2"
+        for line in qf.read_text().splitlines():
+            if not line.startswith("fewshot|"):
+                continue
+            _, ds, j = line.split("|")[:3]
+            npz = str(T / "generated" / ds / "fewshot_n5" / f"draw{j}" / "scsemi5__raw.npz")
+            if npz in done:
+                continue
+            state = "planned"
+            locs.append({"host": host, "path": npz, "state": state, "experiment": "paired_finetune_fewshot_n5_scsemiprofiler",
+                         "cohort": ds, "artifact_role": "inference_output",
+                         "note": "scSemiProfiler 5-pair baseline queued or running on this host at request time"})
+            done.add(npz)
+    req = {
+        "project_id": "pbisc-diffusion",
+        "summary": (
+            "Within-dataset paired fine-tuning benchmark of the final 6k 1x E16 model (plans/paired_finetune_intradataset_20260927): "
+            "LoRA (recommended protocol), full and condition-encoder fine-tuning, sc-only control, LoRA learning curve, "
+            "K-augmentation null, 5-pair deployment scenario, on ImMicro, Ye 2026 sepsis, Imperial ESKD COVID, Giroux 2022 and "
+            "Ji 2025 (whole-blood bulk); scores (scANVI composition, precision/coverage, contrast logFC r, top-50 DEG), figures, "
+            "paired-data manifests, conditions, run receipts and the recommended-protocol LoRA checkpoints. The scSemiProfiler "
+            "5-pair baseline: every finished draw is included as score tables and a completed location; draws not yet "
+            "finished at request time (none on 2026-09-30: 25/25 done) are planned locations. Not uploaded: per-cell count "
+            "arrays and non-recommended fine-tuned checkpoints (sha256 recorded in BUILD/MANIFEST and RUN.json)."),
+        "assets": assets,
+        "inference_locations": locs,
+    }
+    Path(out).write_text(json.dumps(req, indent=1))
+    kinds = {}
+    for a in assets:
+        kinds[(a["host"], a["kind"])] = kinds.get((a["host"], a["kind"]), 0) + 1
+    print(json.dumps({"assets": len(assets), "by_host_kind": {f"{h}/{k}": v for (h, k), v in sorted(kinds.items())},
+                      "locations": len(locs), "location_states": {s: sum(l["state"] == s for l in locs) for s in {l["state"] for l in locs}}},
+                     indent=1))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1]))

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/tools/move_generated.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+# Move already-scored generated NPZs to spark-0110 (same path) to free server_1 disk.  A local NPZ is deleted only
+# after the remote sha256 equals the local sha256 (and the receipt's output_sha256 when the receipt has one); a
+# <name>.moved marker with the sha is left next to the receipt.  usage: move_generated.sh DIR [DIR ...]
+set -uo pipefail
+n_ok=0; n_skip=0; n_fail=0
+for dir in "$@"; do
+  # absolute path: the remote copy must land at the same absolute path (a relative DIR once landed under the remote $HOME)
+  dir=$(realpath -e "$dir") || { echo "FAIL no such dir"; n_fail=$((n_fail+1)); continue; }
+  while IFS= read -r -d '' npz; do
+    rec=${npz%.npz}.log
+    local_sha=$(sha256sum "$npz" | cut -d' ' -f1)
+    want=$(python3 -c "import json,sys;print(json.load(open(sys.argv[1])).get('output_sha256',''))" "$rec" 2>/dev/null || true)
+    if [[ -n $want && $want != "$local_sha" ]]; then echo "SKIP $npz: local sha differs from receipt"; n_skip=$((n_skip+1)); continue; fi
+    ssh -n spark-0110 "mkdir -p '$(dirname "$npz")'" || { n_fail=$((n_fail+1)); continue; }
+    ionice -c3 rsync -a --bwlimit=200000 "$npz" spark-0110:"$(dirname "$npz")/" || { echo "FAIL rsync $npz"; n_fail=$((n_fail+1)); continue; }
+    [[ -s $rec ]] && rsync -a "$rec" spark-0110:"$(dirname "$npz")/"
+    remote=$(ssh -n spark-0110 "sha256sum '$npz' 2>/dev/null" | cut -d' ' -f1)
+    if [[ $remote == "$local_sha" ]]; then
+      rm -f "$npz"; echo "moved to spark-0110 (sha ${local_sha:0:12}) $(date +%FT%T)" > "${npz%.npz}.moved"; n_ok=$((n_ok+1))
+    else
+      echo "FAIL $npz: remote sha ${remote:0:12} != ${local_sha:0:12}; local kept"; n_fail=$((n_fail+1))
+    fi
+  done < <(find "$dir" -maxdepth 1 -name '*.npz' -print0)
+done
+echo "moved=$n_ok skipped=$n_skip failed=$n_fail"

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/sec08_slide_numbers.csv

@@ -0,0 +1,225 @@
+key,value,source
+immicro.coverage.zero_shot,0.192,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.coverage.zs_cal,0.527,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.coverage.lora_n5,0.839,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.coverage.lora_all,0.784,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.coverage.zs_pseudo,0.524,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.zero_shot,0.455,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.zs_cal,0.199,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.lora_n5,0.171,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.lora_all,0.162,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.zs_pseudo,0.204,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.null,0.161,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.lora_5_minus_zero_shot.mean,-0.283,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_5_minus_zero_shot.frac_donors_lower,0.94,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_5_vs_zero_shot.wilcoxon_p,7.2e-18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_minus_zero_shot.mean,-0.267,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_minus_zero_shot.frac_donors_lower,0.92,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_vs_zero_shot.wilcoxon_p,5.8e-18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_minus_zero_shot.mean,-0.283,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_minus_zero_shot.frac_donors_lower,0.94,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_vs_zero_shot.wilcoxon_p,4.2e-18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_minus_zero_shot.mean,-0.283,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_minus_zero_shot.frac_donors_lower,0.95,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_vs_zero_shot.wilcoxon_p,1.2e-18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_minus_zero_shot.mean,-0.293,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_minus_zero_shot.frac_donors_lower,0.96,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_vs_zero_shot.wilcoxon_p,1.7e-18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_5_minus_zs_cal.mean,-0.027,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_5_minus_zs_cal.frac_donors_lower,0.66,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_5_vs_zs_cal.wilcoxon_p,2.3e-05,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_minus_zs_cal.mean,-0.011,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_minus_zs_cal.frac_donors_lower,0.6,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_10_vs_zs_cal.wilcoxon_p,0.057,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_minus_zs_cal.mean,-0.026,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_minus_zs_cal.frac_donors_lower,0.67,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_20_vs_zs_cal.wilcoxon_p,4e-05,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_minus_zs_cal.mean,-0.027,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_minus_zs_cal.frac_donors_lower,0.68,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_40_vs_zs_cal.wilcoxon_p,0.00011,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_minus_zs_cal.mean,-0.037,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_minus_zs_cal.frac_donors_lower,0.69,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.lora_all_vs_zs_cal.wilcoxon_p,1.9e-07,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.zs_cal_minus_zero_shot.mean,-0.256,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.comp_l1.zs_cal_vs_zero_shot.wilcoxon_p,9.5e-17,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_5_minus_zs_cal.mean,0.312,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_5_minus_zs_cal.frac_donors_lower,0.0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_5_vs_zs_cal.wilcoxon_p,2.7e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_10_minus_zs_cal.mean,0.258,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_10_minus_zs_cal.frac_donors_lower,0.01,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_10_vs_zs_cal.wilcoxon_p,2.9e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_20_minus_zs_cal.mean,0.228,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_20_minus_zs_cal.frac_donors_lower,0.03,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_20_vs_zs_cal.wilcoxon_p,5.2e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_40_minus_zs_cal.mean,0.251,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_40_minus_zs_cal.frac_donors_lower,0.03,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_40_vs_zs_cal.wilcoxon_p,5.9e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_all_minus_zs_cal.mean,0.257,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_all_minus_zs_cal.frac_donors_lower,0.04,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.lora_all_vs_zs_cal.wilcoxon_p,7.3e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.zs_cal_minus_zero_shot.mean,0.335,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.zs_cal_vs_zero_shot.wilcoxon_p,2.7e-19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.cal_alpha_fit_pairs,85–86,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/PREPARE.json fold_sizes train+inner_val; tools/prepare_dataset.py fit = fold != f
+immicro.fewshot_n5.coverage.zs_cal_same5,0.535,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/aggregate.json
+immicro.fewshot_n5.coverage.lora5_fixed3k,0.792,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/aggregate.json
+immicro.n_scored_donors,107,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.selected_step.lora_n5.range,2750–5000,RUN.json: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n5/RUN.json
+immicro.n_train.all,78–79,RUN.json: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/real_lora/RUN.json
+immicro.n_inner_val,7,"RUN.json inner_val_rows (all LoRA arms, 5 folds)"
+immicro.folds_cap_limited.lora_n5,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n5/RUN.json
+immicro.folds_cap_limited.lora_n10,1/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n10/RUN.json
+immicro.folds_cap_limited.lora_n20,4/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n20/RUN.json
+immicro.folds_cap_limited.lora_n40,5/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n40/RUN.json
+immicro.folds_cap_limited.real_lora,5/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/real_lora/RUN.json
+immicro.cap_limited.max_val_loss_drop_frac_last_2k_steps,0.0033,history.json inner_val_loss (drop over last patience window / drop from step 0): /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n20/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/lora_n40/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold0/real_lora/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n20/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/lora_n40/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold1/real_lora/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n20/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/lora_n40/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold2/real_lora/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n20/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/lora_n40/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold3/real_lora/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n10/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/lora_n40/history.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fold4/real_lora/history.json
+ye2026_sepsis.coverage.zero_shot,0.067,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.coverage.zs_cal,0.431,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.coverage.lora_n5,0.767,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.coverage.lora_all,0.829,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.coverage.zs_pseudo,0.48,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.zero_shot,0.511,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.zs_cal,0.401,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.lora_n5,0.428,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.lora_all,0.362,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.zs_pseudo,0.233,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.null,0.515,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.lora_5_minus_zero_shot.mean,-0.083,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_5_minus_zero_shot.frac_donors_lower,0.61,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_5_vs_zero_shot.wilcoxon_p,0.042,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_minus_zero_shot.mean,-0.121,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_minus_zero_shot.frac_donors_lower,0.6,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_vs_zero_shot.wilcoxon_p,0.0053,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_minus_zero_shot.mean,-0.131,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_minus_zero_shot.frac_donors_lower,0.62,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_vs_zero_shot.wilcoxon_p,0.0012,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_minus_zero_shot.mean,-0.146,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_minus_zero_shot.frac_donors_lower,0.68,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_vs_zero_shot.wilcoxon_p,0.00026,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_minus_zero_shot.mean,-0.149,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_minus_zero_shot.frac_donors_lower,0.67,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_vs_zero_shot.wilcoxon_p,0.00063,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_5_minus_zs_cal.mean,0.027,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_5_minus_zs_cal.frac_donors_lower,0.5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_5_vs_zs_cal.wilcoxon_p,0.61,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_minus_zs_cal.mean,-0.011,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_minus_zs_cal.frac_donors_lower,0.54,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_10_vs_zs_cal.wilcoxon_p,0.46,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_minus_zs_cal.mean,-0.02,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_minus_zs_cal.frac_donors_lower,0.62,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_20_vs_zs_cal.wilcoxon_p,0.2,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_minus_zs_cal.mean,-0.036,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_minus_zs_cal.frac_donors_lower,0.59,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_40_vs_zs_cal.wilcoxon_p,0.083,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_minus_zs_cal.mean,-0.039,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_minus_zs_cal.frac_donors_lower,0.54,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.lora_all_vs_zs_cal.wilcoxon_p,0.19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.zs_cal_minus_zero_shot.mean,-0.11,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.comp_l1.zs_cal_vs_zero_shot.wilcoxon_p,0.00014,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_5_minus_zs_cal.mean,0.336,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_5_minus_zs_cal.frac_donors_lower,0.0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_5_vs_zs_cal.wilcoxon_p,3.7e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_10_minus_zs_cal.mean,0.355,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_10_minus_zs_cal.frac_donors_lower,0.01,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_10_vs_zs_cal.wilcoxon_p,3.8e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_20_minus_zs_cal.mean,0.376,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_20_minus_zs_cal.frac_donors_lower,0.0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_20_vs_zs_cal.wilcoxon_p,3.7e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_40_minus_zs_cal.mean,0.387,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_40_minus_zs_cal.frac_donors_lower,0.0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_40_vs_zs_cal.wilcoxon_p,3.7e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_all_minus_zs_cal.mean,0.399,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_all_minus_zs_cal.frac_donors_lower,0.0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.lora_all_vs_zs_cal.wilcoxon_p,3.7e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.zs_cal_minus_zero_shot.mean,0.363,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.coverage.zs_cal_vs_zero_shot.wilcoxon_p,3.7e-15,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/per_donor.csv
+ye2026_sepsis.cal_alpha_fit_pairs,65–66,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis/PREPARE.json fold_sizes train+inner_val; tools/prepare_dataset.py fit = fold != f
+ye2026_sepsis.fewshot_n5.coverage.zs_cal_same5,0.428,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/aggregate.json
+ye2026_sepsis.fewshot_n5.coverage.lora5_fixed3k,0.694,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/aggregate.json
+ye2026_sepsis.n_scored_donors,82,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.selected_step.lora_n5.range,2000–3000,RUN.json: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/lora_n5/RUN.json
+ye2026_sepsis.n_train.all,58–59,RUN.json: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/real_lora/RUN.json
+ye2026_sepsis.n_inner_val,7–8,"RUN.json inner_val_rows (all LoRA arms, 5 folds)"
+ye2026_sepsis.folds_cap_limited.lora_n5,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/lora_n5/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/lora_n5/RUN.json
+ye2026_sepsis.folds_cap_limited.lora_n10,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/lora_n10/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/lora_n10/RUN.json
+ye2026_sepsis.folds_cap_limited.lora_n20,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/lora_n20/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/lora_n20/RUN.json
+ye2026_sepsis.folds_cap_limited.lora_n40,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/lora_n40/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/lora_n40/RUN.json
+ye2026_sepsis.folds_cap_limited.real_lora,0/5,RUN.json steps_run == max_steps and steps_run - selected_step < patience*eval_every: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold0/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold1/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold2/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold3/real_lora/RUN.json;/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/ye2026_sepsis/fold4/real_lora/RUN.json
+immicro.nk_fraction_r.zero_shot,0.49,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.zero_shot,-0.01,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.zero_shot,9,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.zs_cal,0.5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.zs_cal,0.12,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.zs_cal,11,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.5,0.2,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.5,0.29,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.5,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.10,0.4,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.10,0.42,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.10,8,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.20,0.56,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.20,0.37,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.20,18,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.40,0.7,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.40,0.48,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.40,22,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.all,0.7,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.all,0.53,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.all,23,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.pseudo,0.6,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.logfc_r.pseudo,0.47,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.pseudo,22,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.logfc_r.broadcast,0.32,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.broadcast,21,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.logfc_r.first_n_above_broadcast,10,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.logfc_r.first_n_above_zero_shot,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.first_n_at_or_above_broadcast,40,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.first_n_above_zero_shot,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.logfc_r.first_n_above_zs_cal,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/contrast.csv
+sepsis.top50_deg.first_n_above_zs_cal,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+immicro.nk_fraction_r.first_n_above_zs_cal,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.coverage.first_n_better_than_zs_cal,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+immicro.comp_l1.first_n_better_than_zs_cal,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json
+ye2026_sepsis.coverage.first_n_better_than_zs_cal,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+ye2026_sepsis.comp_l1.first_n_better_than_zs_cal,10,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+immicro.fraction_r.T.first_n_at_or_above_zero_shot,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.T.zero_shot,0.6,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.T.zs_cal,0.67,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.T.first_n_at_or_above_zs_cal,40,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.T.lora_5_10_20_40_all,0.63/0.60/0.64/0.70/0.74,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.B.first_n_at_or_above_zero_shot,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.B.zero_shot,0.53,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.B.zs_cal,0.72,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.B.first_n_at_or_above_zs_cal,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.B.lora_5_10_20_40_all,0.73/0.72/0.74/0.77/0.75,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.Mono.first_n_at_or_above_zero_shot,all,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.Mono.zero_shot,0.84,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.Mono.zs_cal,0.85,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.Mono.first_n_at_or_above_zs_cal,all,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.Mono.lora_5_10_20_40_all,0.81/0.76/0.79/0.81/0.86,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.NK.first_n_at_or_above_zero_shot,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.NK.zero_shot,0.49,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.NK.zs_cal,0.5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.NK.first_n_at_or_above_zs_cal,20,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+immicro.fraction_r.NK.lora_5_10_20_40_all,0.20/0.40/0.56/0.70/0.70,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/per_donor.csv
+sepsis.top50_deg.T.zero_shot_5_10_20_40_all,3/4/3/0/0/1,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.T.zs_cal,5,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.T.broadcast,3,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.B.zero_shot_5_10_20_40_all,0/0/1/1/3/1,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.B.zs_cal,1,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.B.broadcast,2,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.Mono.zero_shot_5_10_20_40_all,4/1/4/13/12/19,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.Mono.zs_cal,4,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.Mono.broadcast,13,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.NK.zero_shot_5_10_20_40_all,1/0/0/0/0/0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.NK.zs_cal,0,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.NK.broadcast,2,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.DC.zero_shot_5_10_20_40_all,1/0/0/4/7/2,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.DC.zs_cal,1,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.top50_deg.DC.broadcast,1,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/contrast.csv
+sepsis.n_case,61,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+sepsis.n_control,21,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json
+final_checkpoint,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt,RUN.json source_checkpoint (all 50 LoRA runs)
+cal_budget.pattern,"scores/<dataset>/**/summary.json arm names: 'cal' and regex (?:^|[^a-z0-9])n(\d+)(?![0-9]), none of ['lora', 'real', 'pseudo', 'full', 'condenc', 'multik', 'recipe']",/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores
+cal_budget.found,immicro:cal_budget_20261001/fold0:zs__cal_n5;immicro:cal_budget_20261001/fold0:zs__cal_n10;immicro:cal_budget_20261001/fold0:zs__cal_n20;immicro:cal_budget_20261001/fold0:zs__cal_n40;immicro:cal_budget_20261001/fold1:zs__cal_n5;immicro:cal_budget_20261001/fold1:zs__cal_n10;immicro:cal_budget_20261001/fold1:zs__cal_n20;immicro:cal_budget_20261001/fold1:zs__cal_n40;immicro:cal_budget_20261001/fold2:zs__cal_n5;immicro:cal_budget_20261001/fold2:zs__cal_n10;immicro:cal_budget_20261001/fold2:zs__cal_n20;immicro:cal_budget_20261001/fold2:zs__cal_n40;immicro:cal_budget_20261001/fold3:zs__cal_n5;immicro:cal_budget_20261001/fold3:zs__cal_n10;immicro:cal_budget_20261001/fold3:zs__cal_n20;immicro:cal_budget_20261001/fold3:zs__cal_n40;immicro:cal_budget_20261001/fold4:zs__cal_n5;immicro:cal_budget_20261001/fold4:zs__cal_n10;immicro:cal_budget_20261001/fold4:zs__cal_n20;immicro:cal_budget_20261001/fold4:zs__cal_n40;immicro:cal_budget_20261001/pooled:zs__cal_n5;immicro:cal_budget_20261001/pooled:zs__cal_n10;immicro:cal_budget_20261001/pooled:zs__cal_n20;immicro:cal_budget_20261001/pooled:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/fold0:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/fold0:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/fold0:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/fold0:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/fold1:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/fold1:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/fold1:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/fold1:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/fold2:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/fold2:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/fold2:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/fold2:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/fold3:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/fold3:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/fold3:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/fold3:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/fold4:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/fold4:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/fold4:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/fold4:zs__cal_n40;ye2026_sepsis:cal_budget_20261001/pooled:zs__cal_n5;ye2026_sepsis:cal_budget_20261001/pooled:zs__cal_n10;ye2026_sepsis:cal_budget_20261001/pooled:zs__cal_n20;ye2026_sepsis:cal_budget_20261001/pooled:zs__cal_n40,/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores
+immicro.cal_budget.n_drawn,"5,10,20,40",/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores
+ye2026_sepsis.cal_budget.n_drawn,"5,10,20,40",/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/slides_spec.json

@@ -0,0 +1,708 @@
+{
+  "deck": {
+    "output_pptx": "PBISC-Diffusion_paper_summary_20260930.pptx",
+    "paths": "figure paths are relative to the deck folder",
+    "note": "footnote = on-slide text (max 2 lines, may be a shortened copy); footnote_full = section author's full text, put in speaker notes when it differs. Section 9 text comes from data/sec09_slide_text.json; refresh with tools/sync_sec09_text.py after rerunning tools/sec09_paired_validation.py."
+  },
+  "slides": [
+    {
+      "kind": "title",
+      "title": "PBISC-Diffusion: bulk-to-single-cell generation",
+      "subtitle": "Paper summary · 2026-09-30"
+    },
+    {
+      "kind": "agenda",
+      "title": "Outline",
+      "items": [
+        "Model overview: diffusion from bulk to cells",
+        "Backbone: SDD, scDiffusion, bulk conditioning",
+        "Architecture ablation: width, context, gene panel",
+        "SLE benchmark vs scSemiProfiler",
+        "Real bulk ≠ pseudobulk: why zero-shot fails",
+        "Fine-tuning on paired bulk–sc data",
+        "LoRA: compute and ablations (K diversification)",
+        "How many bulk–sc pairs are needed",
+        "Paired-cohort validation vs scSemiProfiler",
+        "Next: cohorts with a paired subset (planned)"
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "1",
+      "section_label": "1 Model overview",
+      "title": "PBISC-Diffusion: bulk in, cells out",
+      "layout": "full",
+      "figures": [
+        "figures/sec01_slide_a.png"
+      ],
+      "keywords": [
+        "Train pair: K-cell pseudobulk → each member cell"
+      ],
+      "footnote": "Fig. 1b redraw. Final model corpus: 33.1M train cells; K = 1–4,000 cells per pseudobulk.",
+      "footnote_full": "Top: deck redraw of manuscript Fig. 1b (larger labels; cells differ by x_T and the Bernoulli support draw, Methods 'Support read-out'). Bottom: corpus of final run f01_6k_1x_seed20260627 (index fingerprint matches). Cells per dataset = base + overlay rows (Model 7 e2_full_source/e2_full_index manifests); 33.1M cells in the donor-level 80/10/10 train split. Registered K grid = 8,192 recipes/epoch. Plus 3,658 top-up recipes over 16 epochs (196-283 per epoch, K 1-4,000), so every train cell is a target once (ALLCELL_DATA_CONTRACT.json coverage_after_topup).",
+      "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_corpus_datasets.csv, data/sec01_recipe_k.csv"
+    },
+    {
+      "kind": "content",
+      "section": "1",
+      "section_label": "1 Model overview",
+      "title": "Denoiser: joint value + support diffusion",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec01_fig1a_denoiser.png"
+      ],
+      "keywords": [
+        "6,000 genes · 54.2M params · Bernoulli support draw"
+      ],
+      "footnote": "Fig. 1a redraw. Final model f01 6k 1x, epoch 16, EMA; 50 DDIM steps.",
+      "footnote_full": "Deck redraw of manuscript Fig. 1a (larger labels, legend dropped). Purple dashed = soft self-conditioning feedback; gold dashed = context to all blocks. Final model f01_6k_1x_seed20260627, checkpoint_epoch_016, EMA; 7 blocks 1024-1024-512-256-512-1024-1024 (effective_config.json, RUN_STATUS.json). Sampling: 50 DDIM steps, η = 0, drawn support read-out, keep gene if u < σ(logit) (methods.tex l.58, l.99). 16 epochs = 16 disjoint 8,192-recipe slots = one pass over the 131,072-recipe pool (manuscript: part 16).",
+      "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_fig1_label_pt.csv"
+    },
+    {
+      "kind": "content",
+      "section": "2",
+      "section_label": "2 Backbone",
+      "title": "Backbone: SDD → PBISC-Diffusion",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec02_backbone_compare.png"
+      ],
+      "keywords": [
+        "Kept from SDD: value + bit, skip-MLP",
+        "Changed: bulk condition, masked loss, Bernoulli read-out"
+      ],
+      "footnote": "SDD: Ostheimer et al., arXiv 2502.02448. scDiffusion: Luo et al., Bioinformatics 2024.",
+      "sources": "tools/sec02_backbone.py; data/sec02_backbone_compare.csv, data/sec02_slide_text.csv, data/refs/",
+      "footnote_full": "Luo et al., Bioinformatics 2024; Ostheimer et al., arXiv 2502.02448 (code 1d5423c). PBISC: final model f01 6k 1x, part 16. σ = sigmoid; Bernoulli is the final read-out; the logit > 0 cut is also reported."
+    },
+    {
+      "kind": "content",
+      "section": "2",
+      "section_label": "2 Backbone",
+      "title": "Bulk conditioning",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec02_bulk_conditioning.png"
+      ],
+      "keywords": [
+        "Same bulk condition for every generated cell",
+        "Final: no cell types, proportions, sum constraint",
+        "18,006 → 512 context into every block"
+      ],
+      "footnote": "Final model f01 6k 1x. Bar heights schematic.",
+      "sources": "tools/sec02_backbone.py; data/sec02_bulk_conditioning.csv, data/sec02_slide_text.csv",
+      "footnote_full": "Final model f01 6k 1x, part 16. Bar heights schematic."
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Width & context ablation: loss saturation",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_width_loss.png"
+      ],
+      "keywords": [
+        "Trunk x4: val loss −31% (0.197 → 0.136)",
+        "ctx x4, prop hint: loss = 1x",
+        "1x flat E8; trunk x4 still falling"
+      ],
+      "footnote": "Old 6k panel (1% floor); one validation set. 16x: memory-floor stop after part 2.",
+      "say": "All arms share the 6k panel (1% floor), corpus, seed and schedule; validation = 192 recipes x 64 cells. ctx x4, both x4 and 16x were read from server_2 snapshots.",
+      "sources": "tools/sec03_width_loss.py; data/sec03_width_loss.csv, data/sec03_width_saturation.csv, data/sec03_width_loss_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Width & context ablation: benchmark",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_width_bench.png"
+      ],
+      "keywords": [
+        "trunk x4: loss −31%, no DEG gain",
+        "Prop hint: oracle = predicted, L1 worse",
+        "1x kept: ties ctx x4, 54M vs 102M"
+      ],
+      "footnote": "SLE, 42 donors, 2,980 genes; old 6k panel. ctx x4: better L1 (0.161 vs 0.197).",
+      "say": "Bars = mean of parts 14-16 with the cut p > 0.5; diamonds = Bernoulli read-out (4 width arms only). Prop hint = 9 L1 cell-type proportions; older panel, not the final model.",
+      "sources": "tools/sec03_width_bench.py; data/sec03_width_bench.csv, data/sec03_width_bench_summary.csv, data/sec03_width_bench_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Gene context: 2k / 3k / 6k / 15k",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_panel.png"
+      ],
+      "keywords": [
+        "6k ≈ 3k: 3k r, DEG inside 6k seed range",
+        "2k best L1; 15k worst r, L1",
+        "6k kept: first panel with CLEC9A (cDC1)"
+      ],
+      "footnote": "SLE, 42 donors, 1,006 genes in every panel. 6k: 5 seeds; base seed best of 5 on r and DEG.",
+      "say": "Runs f01_{2k,3k,6k,p15k}_1x, part 16, Bernoulli, EMA, DDIM 50. Different gene axis from the previous slide, so the two slides cannot be compared. Loss curves are divided by each panel's own part-16 value.",
+      "sources": "tools/sec03_panel.py; data/sec03_panel_bench.csv, data/sec03_panel_seeds.csv, data/sec03_panel_loss.csv, data/sec03_panel_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "4 SLE benchmark",
+      "title": "SLE benchmark: PBISC vs scSemiProfiler",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_benchmark.png"
+      ],
+      "keywords": [
+        "42 held-out donors, sc-pseudobulk input",
+        "Beats scSemiProfiler on all three metrics",
+        "Copying bulk logFC scores higher (backup)"
+      ],
+      "footnote": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_benchmark.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC f01 6k E16, 5 seeds (mean ± SD); baselines 1 draw. scSemiProfiler also gets real sc of 14 in-cohort donors (1,500 cells each). Dashed: input bulk logFC copied to every type (backup)."
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "4 SLE benchmark",
+      "title": "SLE benchmark: per cell type",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_per_celltype.png"
+      ],
+      "keywords": [
+        "Beats scSemiProfiler in T, B, Mono, NK",
+        "Loses in Other; DC r ties, fewer DEGs",
+        "DC: ~15 generated cells per donor"
+      ],
+      "footnote": "Same 42 donors, 1,006 genes. PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run. Other: top-50 of 12.",
+      "say": "PBISC's DC top list in the base seed has only 13 genes, because few DC cells are generated.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_per_celltype.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "Same 42 donors, 1,006 genes. PBISC 5 seeds (mean ± SD), baselines 1 draw. Tick %: share of 251,179 real cells. Other top-50 is out of 12 truth genes. Bulk copy: r 0.68, top-50 113 (backup)."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Real bulk ≠ pseudobulk (model input)",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec05_condition_space.png"
+      ],
+      "keywords": [
+        "Real bulk: 2.46 D from own pseudobulk",
+        "95% of squared offset is one shift",
+        "Shift removed (diagnostic): own donor 7.5% → 75.7%"
+      ],
+      "footnote": "ImMicro, 107 healthy donors. Model input space (log1p CPM). D = median donor-to-donor distance.",
+      "say": "The two pseudobulk clusters on PC1 are sc pools DB1-3 vs DB4-7, so the pairing lines cross mainly because of sc batch. Shift removal subtracts the mean offset of the other 106 donors; a bulk-only deployment would not have them.",
+      "sources": "tools/sec05_condition_space.py; data/sec05_condition_space_numbers.csv, data/sec05_condition_space_coords.csv",
+      "footnote_full": "ImMicro, 107 healthy donors. Model inputs (log1p CPM, 5,508 genes), not learned ctx. D = median donor-to-donor distance; PCA-30 values are lower bounds (full space 3.19 D, 0.91 shared). Shift removal is a diagnostic that uses paired pseudobulks. Pseudobulk PC1 is sc pool batch (pool R² 0.96 on PC1)."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Consequence: cells off-manifold",
+      "layout": "two_figures",
+      "figures": [
+        "figures/sec05_cells_umap.png",
+        "figures/sec05_cells_metrics.png"
+      ],
+      "keywords": [
+        "Monocytes: raw 30.6% vs real 16.7%",
+        "Same noise: 19% of T cells become monocytes",
+        "Precision: raw 0.36 vs pseudobulk 0.92"
+      ],
+      "footnote": "ImMicro, 107 donors; same starting noise for every input. Coverage: raw 0.08, sc pseudobulk 0.51.",
+      "footnote_full": "ImMicro, 107 donors. Final model f01 6k 1× E16 (DDIM 50, EMA); each cell starts from the same noise under every input. Precision/coverage from one scoring call. Composition L1: raw 0.455, pseudobulk 0.166, cohort-mean null 0.161. The UMAP is fitted on real cells and overstates overlap.",
+      "sources": "tools/sec05_cells_offmanifold.py; data/sec05_cells_metrics.csv, data/sec05_cells_umap_coords.csv"
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Consequence: disease direction flips",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec05_behcet_direction.png"
+      ],
+      "keywords": [
+        "CD16 gap: raw −3.97 vs observed +4.94",
+        "C1Q flip vanishes at common depth",
+        "sc pseudobulk: right sign in 5/5 seeds"
+      ],
+      "footnote": "Behçet. Bulk GSE198533 (9 BD / 10 HC); sc GSE198616 (4 / 4, different people) gives observed and pseudobulk.",
+      "say": "Raw bulk gives the wrong sign for CD16 and C1Q in 5/5 seeds. At one common monocyte depth the C1Q gap is −0.08 ± 0.09 pp (1/5 seeds positive), so the C1Q flip is a depth effect; the CD16 flip stays at −3.59. sc pseudobulk of the 8 sc donors gives the observed sign in 5/5 seeds (CD16 +5.48, C1Q +1.82). Observed p is descriptive (4 vs 4). No paired data in this cohort, so no fine-tune here.",
+      "sources": "tools/sec05_behcet_direction.py; data/sec05_behcet_direction.csv",
+      "footnote_full": "Behçet, final model. Bulk GSE198533 (BD 9, HC 10), 5,000 cells per sample; observed = sc GSE198616 (BD 4, HC 4, different people; descriptive p). At common depth, raw CD16 is −3.59 (seed 0) and raw C1Q is −0.08 ± 0.09 pp."
+    },
+    {
+      "kind": "content",
+      "section": "6",
+      "section_label": "6 Paired fine-tuning",
+      "title": "Fine-tuning with paired bulk + sc",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec06_ft_method.png"
+      ],
+      "keywords": [
+        "LoRA 2–14 min vs pretraining 15.9 h"
+      ],
+      "footnote": "Base: final model f01 6k 1x E16. Times from 40 LoRA runs on this host.",
+      "sources": "tools/sec06_finetune_method.py; data/sec06_ft_method.csv",
+      "footnote_full": "Receipts: immicro/fold0/real_lora and ye2026_sepsis/fewshot_n5/draw0/lora RUN.json. Base: f01_6k_1x E16. Times: 40 local LoRA runs."
+    },
+    {
+      "kind": "content",
+      "section": "7",
+      "section_label": "7 LoRA ablations",
+      "title": "Full vs LoRA vs cond.-encoder-only",
+      "layout": "full",
+      "figures": [
+        "figures/sec07_ft_modes.png"
+      ],
+      "keywords": [
+        "LoRA ≈ full FT, 42× fewer params",
+        "Cond.-encoder-only ≈ ZS + cal. on ImMicro",
+        "LoRA beats both on coverage"
+      ],
+      "footnote": "Fold 0, cond.-enc. vs ZS + cal.: coverage 0.73 vs 0.73 / 0.74 vs 0.63, L1 p 0.06 / 0.93 (ImMicro / sepsis).",
+      "sources": "tools/sec07_ft_modes.py; data/sec07_ft_modes_accuracy.csv, data/sec07_ft_modes_compute.csv, data/sec07_ft_modes_wilcoxon.csv",
+      "footnote_full": "Base f01_6k_1x E16. ImMicro / sepsis; paired Wilcoxon on donor values, uncorrected. Full vs LoRA L1 p 0.25 / 0.71 (pooled); 42× fewer trainable params (1.3 M). Zero-shot + calibration: α_g = mean CPM(pseudobulk_g) / mean CPM(bulk_g) over the fold's 85–86 / 65–66 paired training donors (train + inner-val); bulk CPM × α, renormalised. The sc-only FT never saw real bulk, so it is fed calibrated bulk. Raw → calibrated zero-shot (pooled): L1 0.45 → 0.20 / 0.51 → 0.40, coverage 0.19 → 0.53 / 0.07 → 0.43, sepsis logFC r −0.01 → 0.12. LoRA vs ZS + cal. (pooled): coverage 0.78 vs 0.53 / 0.83 vs 0.43 (p 7e-19 / 4e-15); L1 0.16 vs 0.20 (p 2e-7) / 0.36 vs 0.40 (p 0.19); logFC r 0.53 vs 0.12. Fold 0, cond.-enc. vs ZS + cal.: L1 0.20 vs 0.17 (p 0.06) / 0.38 vs 0.39 (p 0.93), coverage 0.73 vs 0.73 (p 0.68) / 0.74 vs 0.63 (p 7e-4). Fold 0, LoRA: coverage 0.91 / 0.86; vs cond.-enc. p 4e-5 / 1e-3, vs ZS + cal. p 6e-5 / 3e-4; L1 vs cond.-enc. p 3e-5 / 0.52, vs ZS + cal. p 0.29 / 0.96. sc-only FT (+ cal.), pooled: coverage 0.62 / 0.73, logFC r 0.30. Open = fold-0 pilot; compare same marker only. Per-step time: runs early-stop differently."
+    },
+    {
+      "kind": "content",
+      "section": "7",
+      "section_label": "7 LoRA ablations",
+      "title": "K diversification: no gain",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec07_k_aug.png"
+      ],
+      "keywords": [
+        "No gain; keep single K 1000",
+        "Multi-K slightly worse on ImMicro (p 0.04)",
+        "Inner-val loss Δ ≤ 1.2e-4"
+      ],
+      "footnote": "LoRA r16, 5-fold pooled. p = paired Wilcoxon on donor L1, uncorrected.",
+      "sources": "tools/sec07_k_aug.py; data/sec07_k_aug.csv, data/sec07_k_aug_wilcoxon.csv, data/sec07_k_aug_inner_val_delta.csv",
+      "footnote_full": "LoRA r16 on f01_6k_1x E16, 5-fold pooled. p: paired Wilcoxon, donor L1 vs plain LoRA, uncorrected. Recipes arm: K 1000 real bulk + ½-batch pretraining pseudobulk, no multi-K."
+    },
+    {
+      "kind": "content",
+      "section": "8",
+      "section_label": "8 Pairs needed",
+      "title": "Pairs needed: composition, coverage, training steps",
+      "layout": "full",
+      "figures": [
+        "figures/sec08_pairs_realism.png"
+      ],
+      "keywords": [
+        "Calibration lifts coverage to 0.53 / 0.43",
+        "More pairs do not help calibration",
+        "LoRA, 5+7 pairs: coverage 0.84 / 0.77"
+      ],
+      "footnote": "ImMicro / sepsis, 5-fold CV (107 / 82 donors). N+7 = N training + 7 early-stop pairs; squares: same pairs.",
+      "footnote_full": "0 = no LoRA training: zero-shot on real bulk (pale) and zero-shot + calibration (dark blue). Calibration α_g = mean CPM(pseudobulk_g) / mean CPM(bulk_g) over all paired training donors of the fold (train + inner-val: 85–86 / 65–66, the same donors LoRA 'all' trains and early-stops on); bulk CPM × α, renormalised. ImMicro L1 axis is broken (zero-shot 0.455). Raw → calibrated: coverage 0.19 → 0.53 / 0.07 → 0.43, L1 0.455 → 0.199 / 0.511 → 0.401. L1 at 5+7 pairs vs raw zero-shot: 0.171 vs 0.455 (ImMicro, p 7e-18), 0.428 vs 0.511 (sepsis, p 0.04). LoRA vs ZS + cal.: coverage higher at every n in both cohorts (p ≤ 4e-15); L1 lower on ImMicro (all pairs 0.162 vs 0.199, p 2e-7) but not on sepsis (0.362 vs 0.401, p 0.19; 5+7 pairs 0.428). With α from the same 5 pairs (few-pair call, its own donors; LoRA fixed 3k steps, no inner-val): coverage 0.54 vs LoRA 0.79 (ImMicro), 0.43 vs 0.69 (sepsis). 20k step cap reached by ImMicro folds 1/5 (10), 4/5 (20), 5/5 (40, all); last 2k steps <0.4% of total loss drop. Squares: zero-shot + calibration with alpha fitted on exactly the pairs each LoRA run saw (N training + its inner-val pairs). Coverage stays 0.52-0.53 (ImMicro) and 0.43-0.44 (sepsis) from 5+7 pairs to all; composition L1 0.19-0.20 / 0.40-0.41. LoRA vs calibration at equal pairs, donor L1: ImMicro better at every n (p <= 0.03); sepsis 5+7 not different (0.428 vs 0.412, p 0.68), lower from 10+7 but not significant (p 0.08-0.32).",
+      "say": "Keywords say +7; 2 of 5 sepsis folds use 8 inner-val pairs.",
+      "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_realism.csv, data/sec08_selected_steps.csv, data/sec08_slide_numbers.csv"
+    },
+    {
+      "kind": "content",
+      "section": "8",
+      "section_label": "8 Pairs needed",
+      "title": "Pairs needed: donor and disease signal",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec08_pairs_signal.png"
+      ],
+      "keywords": [
+        "Calibration alone: logFC r ≤ 0.12",
+        "LoRA r above broadcast from 10+7 pairs",
+        "LoRA DEG passes calibration at 20+7"
+      ],
+      "footnote": "Sepsis: 61 case vs 21 control. logFC r = mean over T / B / Mono / NK. Raw zero-shot: logFC r −0.01, DEG 9.",
+      "footnote_full": "Zero-shot on real bulk: logFC r −0.01, DEG 9, NK r 0.49; zero-shot + calibration (α from all 85–86 / 65–66 fold pairs): 0.12, 11, 0.50; LoRA with all pairs: 0.53, 23, 0.70. LoRA passes ZS + cal. from 5+7 pairs on logFC r (0.29), from 20+7 on DEG (18) and from 20+7 on NK r (0.56). ≤10 pairs: NK r (0.20/0.40) and DEG (5/8) below both zero-shot points. ImMicro fraction r: T at or above raw zero-shot from 20 pairs, at or above ZS + cal. (0.67) from 40; Mono only with all. DEG gain is Mono/DC; T overlap 3 (zero-shot), 5 (ZS + cal.) → 0–1 from 20 pairs; NK 0 at every LoRA n (broadcast T 3, NK 2). Squares: calibration with alpha from the same pairs as each LoRA run. Sepsis logFC r 0.08 / 0.10 / 0.10 / 0.12 (5 / 10 / 20 / 40 + 7 pairs) vs LoRA 0.29 / 0.42 / 0.37 / 0.48; partial r about 0 vs 0.25-0.40; top-50 DEG 12 / 10 / 7 / 7 vs 5 / 8 / 18 / 22. ImMicro NK r 0.50-0.53 at every n vs LoRA 0.20 / 0.40 / 0.56 / 0.70.",
+      "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_signal.csv, data/sec08_slide_numbers.csv"
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 0,
+      "title": "Five paired cohorts",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_cohorts.png"
+      ],
+      "keywords": [
+        "403 paired samples, 275 donors",
+        "Only identity-pass pairs; donor-level splits",
+        "Ji bulk: whole blood, not PBMC"
+      ],
+      "footnote": "Usable pair = passed identity check and ≥ 200 cells: 403 of 430 pairs; donor-level splits.",
+      "footnote_full": "Usable pair = passed the pairing identity check and >= 200 cells (fold >= 0 in data/<cohort>/folds.csv); dropped 15 identity-fail, 11 borderline and 1 low-cell pairs of 430. ImMicro: no identity flag, all used. Imperial, Giroux, Ji: several pairs per donor. Case vs control = samples in scores/<cohort>/pooled/summary.json. Giroux 'moderate' = GEO 'severe' (paper term); its moderate vs mild subjects are 4F/0M vs 0F/5M (scored), so its disease metrics exclude sex-linked genes. Labels: data/<cohort>/QC_REPORT.md; ImMicro: GEO GSE314922/GSE314416.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_cohorts.csv, data/sec09_quoted_numbers.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "403 paired samples (sum of usable rows)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_usable_rows)"
+        },
+        {
+          "value": "275 donors (sum of usable donors)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_usable_donors)"
+        },
+        {
+          "value": "dropped 15 identity-fail, 11 borderline, 1 < 200 cells, of 430",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/features.npz (identity_pass, identity_check); samples.csv (n_cells); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/prepare_dataset.py (MIN_CELLS)"
+        },
+        {
+          "value": "ImMicro 107 pairs / 107 donors",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/DRAWS.json"
+        },
+        {
+          "value": "Sepsis 82 pairs / 82 donors; sepsis 61 vs control 21",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json"
+        },
+        {
+          "value": "Imperial 157 pairs / 57 donors; COVID+ 113 vs COVID− 34 (+10 recovery, neither)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/pooled/summary.json"
+        },
+        {
+          "value": "Giroux 24 pairs / 9 donors; moderate 11 vs mild 13",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/pooled/summary.json"
+        },
+        {
+          "value": "Ji 33 pairs / 20 donors; ChILI 18 vs ICI control 15",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 1,
+      "title": "5 donors: LoRA vs scSemiProfiler — cells",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_fewpair_cells.png"
+      ],
+      "keywords": [
+        "LoRA-5 > scSemi-5: coverage, precision 5/5",
+        "Beats zero-shot + calibration: coverage 5/5",
+        "Lower L1 than calibration: ImMicro only"
+      ],
+      "footnote": "5 draws per cohort; test = all other usable pairs. Calibration α from the same 5 donors. scANVI labels.",
+      "footnote_full": "Base f01 6k·1x·E16 (EMA, DDIM 50) + LoRA r=16, 3,000 steps. LoRA-5 / scSemi-5 = LoRA / scSemiProfiler trained on the same 5 donors (5–15 pairs); test = all other usable pairs. Zero-shot = base model without fine-tuning, fed each test pair's real bulk as measured; zero-shot + calibration = the same model fed that bulk after gene-wise calibration, alpha fitted on the same 5 training donors' pairs (both in the same call as LoRA-5). scSemi-5 scored in a separate call, same test set and reference (sha-checked); 25/25 draws. Verdict = better in >= 80% of shared draws. LoRA-5 lower L1 than scSemi-5 in 4/5 (Giroux split 3/5). LoRA-5 > zero-shot + calibration: coverage 5/5, precision 3/5, depth-matched 5/5 and 3/5 cohorts; > zero-shot (real bulk): coverage 5/5, precision 5/5, depth-matched 5/5 and 4/5. Depth-matched P/C vs scSemi-5: same ranking, coverage LoRA-5 0.83–0.97 vs scSemi-5 0.15–0.28. Composition L1, LoRA-5 minus zero-shot + calibration, per-donor Wilcoxon (gain = p < 0.05): ImMicro -0.03 (p 2e-10), Sepsis +0.02 (p 0.79), Imperial +0.02 (p 0.38), Giroux -0.06 (p 0.38), Ji -0.01 (p 0.70); minus zero-shot (real bulk): ImMicro -0.27 (p 2e-18), Sepsis -0.04 (p 0.15), Imperial -0.35 (p 2e-08), Giroux -0.11 (p 0.04), Ji -0.23 (p 0.02). scANVI labels; P/C = PCA-50, k=10 balls. Scores: scores/<cohort>/fewshot_n5/.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_cells.csv, data/sec09_scsemi_status.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "LoRA-5 vs scSemi-5 coverage (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.036 (5/5, lora); Sepsis 0.694 vs 0.080 (5/5, lora); Imperial 0.737 vs 0.059 (5/5, lora); Giroux 0.961 vs 0.607 (5/5, lora); Ji 0.707 vs 0.145 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 precision (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.518 (5/5, lora); Sepsis 0.906 vs 0.728 (5/5, lora); Imperial 0.853 vs 0.330 (5/5, lora); Giroux 1.000 vs 0.992 (5/5, lora); Ji 0.978 vs 0.840 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 comp_l1 (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.233 (5/5, lora); Sepsis 0.466 vs 0.612 (5/5, lora); Imperial 0.503 vs 0.602 (5/5, lora); Giroux 0.256 vs 0.270 (3/5, split); Ji 0.513 vs 0.592 (4/5, lora) -> LoRA verdicts 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 coverage_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.284 (5/5, lora); Sepsis 0.834 vs 0.158 (5/5, lora); Imperial 0.863 vs 0.152 (5/5, lora); Giroux 0.974 vs 0.238 (5/5, lora); Ji 0.908 vs 0.150 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 precision_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.829 (5/5, lora); Sepsis 0.914 vs 0.504 (5/5, lora); Imperial 0.881 vs 0.429 (5/5, lora); Giroux 0.995 vs 0.426 (5/5, lora); Ji 0.974 vs 0.564 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.535 (5/5, lora); Sepsis 0.694 vs 0.428 (5/5, lora); Imperial 0.737 vs 0.634 (5/5, lora); Giroux 0.961 vs 0.944 (5/5, lora); Ji 0.707 vs 0.114 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.904 (5/5, lora); Sepsis 0.906 vs 0.899 (3/5, split); Imperial 0.853 vs 0.845 (3/5, split); Giroux 1.000 vs 0.996 (4/5, lora); Ji 0.978 vs 0.782 (5/5, lora) -> LoRA verdicts 3/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.203 (5/5, lora); Sepsis 0.466 vs 0.446 (2/5, split); Imperial 0.503 vs 0.506 (2/5, split); Giroux 0.256 vs 0.288 (2/5, split); Ji 0.513 vs 0.514 (3/5, split) -> LoRA verdicts 1/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.780 (5/5, lora); Sepsis 0.834 vs 0.658 (5/5, lora); Imperial 0.863 vs 0.681 (5/5, lora); Giroux 0.974 vs 0.937 (5/5, lora); Ji 0.908 vs 0.357 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.940 (5/5, lora); Sepsis 0.914 vs 0.909 (3/5, split); Imperial 0.881 vs 0.868 (3/5, split); Giroux 0.995 vs 0.989 (5/5, lora); Ji 0.974 vs 0.658 (5/5, lora) -> LoRA verdicts 3/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.190 (5/5, lora); Sepsis 0.694 vs 0.076 (5/5, lora); Imperial 0.737 vs 0.114 (5/5, lora); Giroux 0.961 vs 0.472 (5/5, lora); Ji 0.707 vs 0.066 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.677 (5/5, lora); Sepsis 0.906 vs 0.625 (5/5, lora); Imperial 0.853 vs 0.500 (5/5, lora); Giroux 1.000 vs 0.980 (5/5, lora); Ji 0.978 vs 0.799 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.445 (5/5, lora); Sepsis 0.466 vs 0.510 (5/5, lora); Imperial 0.503 vs 0.806 (5/5, lora); Giroux 0.256 vs 0.351 (5/5, lora); Ji 0.513 vs 0.721 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.213 (5/5, lora); Sepsis 0.834 vs 0.073 (5/5, lora); Imperial 0.863 vs 0.151 (5/5, lora); Giroux 0.974 vs 0.549 (5/5, lora); Ji 0.908 vs 0.199 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.671 (5/5, lora); Sepsis 0.914 vs 0.595 (5/5, lora); Imperial 0.881 vs 0.561 (5/5, lora); Giroux 0.995 vs 0.996 (2/5, split); Ji 0.974 vs 0.480 (5/5, lora) -> LoRA verdicts 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "coverage cohort means over all draws: LoRA-5 ImMicro 0.792, Sepsis 0.694, Imperial 0.737, Giroux 0.961, Ji 0.707; scSemi-5 ImMicro 0.036, Sepsis 0.080, Imperial 0.059, Giroux 0.607, Ji 0.145; zero-shot + calibration ImMicro 0.535, Sepsis 0.428, Imperial 0.634, Giroux 0.944, Ji 0.114; zero-shot ImMicro 0.190, Sepsis 0.076, Imperial 0.114, Giroux 0.472, Ji 0.066",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 minus zero-shot + calibration composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.027, n=107, p=2.2e-10; Sepsis +0.018, n=82, p=0.79; Imperial +0.016, n=57, p=0.38; Giroux -0.065, n=8, p=0.38; Ji -0.012, n=20, p=0.7 -> gain in 1/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/folds.csv"
+        },
+        {
+          "value": "LoRA-5 minus zero-shot (real bulk) composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.270, n=107, p=2.2e-18; Sepsis -0.044, n=82, p=0.15; Imperial -0.348, n=57, p=1.6e-08; Giroux -0.107, n=8, p=0.039; Ji -0.232, n=20, p=0.017 -> gain in 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/folds.csv"
+        },
+        {
+          "value": "scSemiProfiler draws used / LoRA draws: ImMicro 5/5, Sepsis 5/5, Imperial 5/5, Giroux 5/5, Ji 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_scsemi_status.csv"
+        },
+        {
+          "value": "training pairs per draw: ImMicro 5, Sepsis 5, Imperial 12–15, Giroux 12–14, Ji 7–9; test pairs: ImMicro 102, Sepsis 77, Imperial 142–145, Giroux 10–12, Ji 24–26",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_train_rows, n_test_rows)"
+        },
+        {
+          "value": "LoRA r = 16; 3000 fixed steps; base runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt; sampling: test donors only, 1,500 cells each, seed 20260927, DDIM 50, EMA, Bernoulli read-out",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fewshot_n5/draw0/lora/RUN.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md"
+        },
+        {
+          "value": "P/C definition: precision/coverage/density (PCA-50 + k = 10 balls fitted on the scored donors' observed R1 cells); PCA components in every summary.json = 50",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 2,
+      "title": "5 donors: LoRA vs scSemiProfiler — disease signal",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec09_fewpair_disease.png"
+      ],
+      "keywords": [
+        "LoRA-5 > scSemi-5: DEG 2/4, logFC r 2/4",
+        "logFC r: LoRA-5 > calibrated zero-shot 4/4",
+        "Giroux: broadcast r beats all arms"
+      ],
+      "footnote": "Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M). Top-50 DEG over 5 types.",
+      "footnote_full": "Same draws and verdict rule as the previous slide; DEG tie = half the draws equal or mean gap < 1 gene. logFC r = mean over T/B/Mono/NK of case-vs-control log2FC r (generated vs observed); top-50 DEG = Welch+BH overlap summed over 5 types (max 250; Giroux 1–63, Ji 75–250 per draw). Giroux test = 2 vs 2 subjects per draw. LoRA-5-better draws (verdict): vs scSemi-5 logFC r Sepsis split 3/5, Imperial lora 5/5, Giroux scSemi-5 0/5, Ji lora 5/5; DEG Sepsis lora 5/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot + calibration: logFC r Sepsis lora 4/5, Imperial lora 5/5, Giroux lora 4/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot (real bulk): logFC r Sepsis lora 5/5, Imperial lora 5/5, Giroux split 3/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Broadcast = the input real bulk's own logFC. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_disease.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "Giroux sex by contrast arm (subjects, F/M): scored moderate 4F/0M vs mild 0F/5M; all paired 4F/1M vs 0F/5M",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/features.npz (sex_bulk_expr = sex_sc_expr = sex_geo); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv"
+        },
+        {
+          "value": "Giroux sex-linked genes excluded from the disease metrics: 196 listed (43 chrY non-PAR + 153 X escape), 65 of 5563 scoring genes",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/sex_linked_genes_exclude.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/BUILD.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json (gene_exclusion)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 5.2 (5/5, 0 tied, lora); Imperial 113.6 vs 46.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 9.4 (4/5, 0 tied, lora); Imperial 113.6 vs 103.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 10.0 (4/5, 0 tied, lora); Imperial 113.6 vs 105.6 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.402 (3/5, split); Imperial 0.749 vs 0.562 (5/5, lora); Giroux 0.297 vs 0.397 (0/5, other); Ji 0.333 vs 0.153 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.096 (4/5, lora); Imperial 0.749 vs 0.676 (5/5, lora); Giroux 0.297 vs 0.228 (4/5, lora); Ji 0.333 vs 0.140 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs -0.024 (5/5, lora); Imperial 0.749 vs 0.638 (5/5, lora); Giroux 0.297 vs 0.255 (3/5, split); Ji 0.333 vs 0.087 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Sepsis broadcast logFC r 0.320 vs best arm 0.402; broadcast DEG 20.0 vs best arm 16.4 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Imperial broadcast logFC r 0.656 vs best arm 0.749; broadcast DEG 74.8 vs best arm 113.6 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Giroux broadcast logFC r 0.492 vs best arm 0.397; broadcast DEG 0.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Ji broadcast logFC r 0.234 vs best arm 0.333; broadcast DEG 1.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "DEG maximum per draw: Sepsis 250, Imperial 250, Giroux 1–63, Ji 75–250",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (deg_top50_max; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex)"
+        },
+        {
+          "value": "Giroux test subjects per draw: 2 vs 2 (moderate vs mild); test pairs 10–12",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json (train_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv (group)"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 3,
+      "title": "All pairs, 5-fold",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_fivefold.png"
+      ],
+      "keywords": [
+        "LoRA tops coverage 5/5, logFC r 4/4",
+        "Ji whole blood: LoRA 0.86, others 0.06–0.11",
+        "Giroux: broadcast r 0.58 > LoRA 0.46"
+      ],
+      "footnote": "Donor-level 5-fold CV. Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M).",
+      "footnote_full": "Donor-level 5-fold CV; each pair scored once, by the fold model that never trained on its donor (one pooled call per cohort). LoRA r=16 on all training donors' real bulk, early stop on inner-val donors. Zero-shot = base model without fine-tuning, fed the test pair's real bulk as measured; zero-shot + calibration = the same model fed gene-wise calibrated bulk (alpha fitted on the fold's training donors); sc-only fine-tune (+ calibration) = full fine-tune on training donors' sc pseudobulk, fed calibrated bulk. Base f01 6k·1x·E16. LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4. Broadcast = raw real bulk. Sepsis DEG from pooled_deg. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores/<cohort>/pooled/summary.json.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fivefold.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "Ji coverage: zero-shot 0.055, zero-shot + calibration 0.106, sc-only (+ calibration) 0.113, LoRA 0.857",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json"
+        },
+        {
+          "value": "Giroux logFC r: broadcast 0.575 vs LoRA 0.461; DEG broadcast 5 vs LoRA 5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json"
+        },
+        {
+          "value": "LoRA logFC r vs broadcast: Sepsis 0.527 vs 0.316, Imperial 0.819 vs 0.661, Giroux 0.461 vs 0.575, Ji 0.479 vs 0.240",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "coverage LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): ImMicro 0.784 / 0.527 / 0.192 / 0.623, Sepsis 0.829 / 0.431 / 0.067 / 0.726, Imperial 0.850 / 0.639 / 0.112 / 0.555, Giroux 0.928 / 0.906 / 0.281 / 0.908, Ji 0.857 / 0.106 / 0.055 / 0.113",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "logFC r LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 0.527 / 0.123 / -0.012 / 0.296, Imperial 0.819 / 0.679 / 0.644 / 0.808, Giroux 0.461 / 0.269 / 0.334 / 0.344, Ji 0.479 / 0.142 / 0.084 / 0.245",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "top-50 DEG LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 23 / 11 / 9 / 15, Imperial 143 / 111 / 106 / 129, Giroux 5 / 0 / 1 / 0, Ji 15 / 0 / 0 / 0",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "Pooled n (pairs / donors): ImMicro 107/107, Sepsis 82/82, Imperial 157/57, Giroux 24/9, Ji 33/20",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (n_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json"
+        },
+        {
+          "value": "early stopping: About 10 % of each fold's training donors are inner-validation, used only for early stopping",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "10",
+      "section_label": "10 Paired-subset cohorts",
+      "title": "Next: cohorts with a paired subset (planned)",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec10_paired_subset_cohorts.png"
+      ],
+      "keywords": [
+        "Train LoRA on pairs, generate bulk-only",
+        "Sepsis controls: 21 paired + 54 bulk-only",
+        "ChILI pre-ICI: 11 bulk, 4 unpaired sc"
+      ],
+      "footnote": "Counts from pairing manifests. Model: f01 6k 1x E16 + LoRA r16.",
+      "say": "Planned: no LoRA cells exist yet for any bulk-only sample. Ji bulk is PAXgene whole blood. Only 7 of the Arunachalam pairs are strict same-visit pairs. Asan is omitted because it has no donor map.",
+      "sources": "tools/sec10_paired_subset_cohorts.py; data/sec10_cohort_counts.csv, data/sec10_bulk_only_groups.csv, data/sec10_quoted_numbers.csv",
+      "footnote_full": "Counts from pairing manifests. QC = identity check (Imperial also >=200 cells); sex-flag = bulk sex differs from metadata or is ambiguous. Not shown: Asan (no donor map), Giroux (24 pairs, 17 bulk-only; broadcast beats LoRA). Model: f01 6k 1x E16 + LoRA r16."
+    },
+    {
+      "kind": "divider",
+      "title": "Backup"
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "Backup · 4 SLE benchmark",
+      "backup": true,
+      "title": "Bulk broadcast baseline",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_broadcast.png"
+      ],
+      "keywords": [
+        "Bulk logFC copied: r 0.68 vs 0.37",
+        "Type-specific part: partial r 0.21",
+        "scSemiProfiler: r 0.32, partial r 0.17"
+      ],
+      "footnote": "SLE, 42 donors, 1,006 genes. Partial r: both logFCs residualised on bulk logFC.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_broadcast.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "SLE held-out, 42 donors, 1,006 genes; PBISC 5 seeds. Partial r: truth and method logFC residualised on bulk logFC. Broadcast has no residual, so it is not scored. Split-half and BayesPrism checks: sle_broadcast/RESULTS.md."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "Backup · 5 Real bulk vs pseudobulk",
+      "backup": true,
+      "title": "Same shift in three paired cohorts",
+      "layout": "full",
+      "figures": [
+        "figures/sec05_shift_cohorts.png"
+      ],
+      "keywords": [
+        "Shared shift in all 3 cohorts"
+      ],
+      "footnote": "Model inputs only, no generation. PCA fitted per cohort on sc pseudobulks.",
+      "sources": "tools/sec05_shift_cohorts.py; data/sec05_shift_cohorts.csv",
+      "footnote_full": "Model inputs only, no generation. PCA is fitted on each cohort's sc pseudobulks (Arunachalam: 11 PCs, n = 12). D uses same-condition pairs only for Randolph and Arunachalam. Shift removal is a leave-one-out diagnostic."
+    },
+    {
+      "kind": "content",
+      "section": "6",
+      "section_label": "Backup · 6 Paired fine-tuning",
+      "backup": true,
+      "title": "Evaluation design",
+      "layout": "full",
+      "figures": [
+        "figures/sec06_eval_design.png"
+      ],
+      "keywords": [
+        "Donor-level splits; test donors never trained",
+        "5-fold: other folds · few: 5 donors",
+        "Giroux few-pair trains 12–14 of 24 pairs"
+      ],
+      "footnote": "Bars: Ye 2026 sepsis. QC-failed pairs excluded.",
+      "sources": "tools/sec06_finetune_method.py; data/sec06_eval_design.csv, data/sec06_eval_design_sepsis_roles.csv",
+      "footnote_full": "Counts: data/<cohort>/folds.csv and fewshot_n5/DRAWS.json; QC-failed pairs (fold −1) excluded. Bars: Ye 2026 sepsis."
+    }
+  ]
+}