Digital Drosophila Archive
62 lines
3.3 KiB
Markdown
62 lines
3.3 KiB
Markdown
# Phase 4AR read-only progress and post-run audit
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These tools are separate from the 16 registered scientific source files. They
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never launch a brain, change plastic delta, resample inputs, resume a partial
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epoch or authorize another assay. Tests use synthetic temporary data only.
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## While the experiment runs
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`progress_normal.py` reads the small committed ledger records. It distinguishes
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completed CPU-verified epochs, completed probes, pending processes and mere
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result files. It is not a whole-tree integrity check or a learning gate.
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`normal_finalize.py check` makes a real Linux process query and examines the
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actual terminal receipts. It intentionally refuses a live or incomplete run,
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even when a completion-looking file exists. An absent PID alone proves nothing.
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No final-audit directory or result is created by this check.
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## After all registered work has ended
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An operator must first provision this exact, empty, separately owned directory:
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`/data/digital-drosophila/experiments/phase4ar-normal-conditioning-final-audit/`
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Then run the committed `normal_finalize.py audit` with the existing science
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environment and `-B`. The driver refuses uncommitted audit code, a nonempty
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destination, missing/failed exit evidence, a live runtime or a stopped phase.
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There is no automatic retry after a partial finalization.
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The terminal ledger's authentic original PASS was written before
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`audit/execution-complete.json` existed. A fresh unmodified ledger audit will
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therefore report that exact one extra file. `terminal_ledger.py` retains both
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original reports unchanged. It accepts this boundary only if every other field,
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the full order, head, payload and compiler-cache identity agree, and both byte
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totals differ by exactly that completion file's actual length. It never deletes
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the file or changes a failure flag to PASS. Any other difference is rejected.
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The terminal inventory includes all experiment payload, registered external
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inputs and all six administrative supervisor files. Startup identities were
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independently bound while the run was live. The driver additionally checks real
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process quiescence before and after analysis; file existence is not a lock or
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an atomic filesystem snapshot.
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The frozen `normal_metrics.analyze` then runs in a fresh CPU process against all
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33,920 task records and 3,920 probes. Its real exit and file bytes are recorded.
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The old frozen data are reverified in a separate integrity process. Full
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terminal inventory is read again after analysis. All new reports and their
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manifest/checksums live outside the original experiment.
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`normal_outcome.py` checks the internally consistent fixed terminal report:
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all eight cells, the same-replicate conjunction, all controls and both H control
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source mappings. Incomplete/invalid evidence is not a scientific negative result.
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It cannot authenticate a caller's evidence flags itself; the driver must supply
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the actual verified files and process evidence.
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Even `NORMAL-ACQUISITION-PASS` is **not usable learning** or a model candidate.
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Delayed reward, reversal and retention remain unrun and are not automatically
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authorized. No parameter, seed or endpoint is chosen using intermediate results.
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Historical integrity checks do not rerun the five old reference trials and are
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never labeled as new numerical regression. Git stores code and selected small
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evidence, not a second copy of the bulk experiment dataset.
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