Quorum Sensing computational Evaluate
status 1 · Plancomplete 2 · Executionran 3 · Evidence 4 · Quality 5 · Decision 6 · Release Implementationcomplete
Q Does autoinducer feedback switch a dense colony ON via Hill activation?
Claim
Does autoinducer feedback switch a dense colony ON via Hill activation?
Test
Result
Model composition
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Analyses to run — which of the workspace's ANALYSIS_REGISTRY entries to compute on dispatch
Runnable models — the composite(s) this study simulates
viva_munk.composites.quorum_sensing
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Simulations
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Results
What came back from this study's runs — a per-store preview of the latest run, plus the raw emitter store (zarr/parquet/runs.db) for every run.
Latest run preview
Per-store sparkline + first/last/min/max from the most recent run's emitted trajectory. This is a downsampled PREVIEW, not the full data — use the per-store or Download-all links for the complete arrays.
Analyses
Result files written by this study's analysis steps.
Readouts
The Design-time emit contract: what the emitter is configured to persist, which store paths it declares, and the shape of what lands on disk. No observed values here — see Results for what actually came back from a run.
Emitter & config
The emitter class, its write-path module, emit
interval/buffer, output location, and emit scope — from
lib/emitters.py and this study's (or the workspace's)
runtime.default_emitter.
Emitted paths
Each row is an emitter store_path
the composite will save; authored entries in study.yaml add names,
descriptions, and units on top.
Outputs & shapes
Store path, dtype, shape
(n_steps, …), units, and a best-effort byte estimate for each emitted
leaf.
Tests
The bar. What must be true for this model to count as correct — the acceptance criteria, their bands, and the report cards that grade them.
Report cards
The compiled output of this study’s Tests — per-test verdict and the change since the previous run.
Behavioral tests
true
data_source: latest_run
- No
expected_behavior:entries declared in study.yaml yet. Add some to see them appear here as test rows. Click "Run tests" to discover and execute additionalstudies/<slug>/tests/test_*.pyfiles alongside the auto-generated assertions.
Audit
Is the bar sound? Whether the tests themselves are rigorous and matched to the problem — sufficiency, where the model was sourced, and the rigor + reproducibility scorecards.
Build
Was it earned? The agentic model-building loop that iterated the model to clear the bar — the draft, the frozen tests, the iteration-by-iteration trajectory, and the result (or an honest give-up).
Verdict & conclusion
○ unattributed
🔒 frozen 2026-09-24 02:13 UTC ·
21ebac1a8f