Overview
BeeStack is an executable, evidence-typed research scaffold for whole-colony simulation of the Western honey bee (Apis mellifera), organized as five layers (Body, Brain, Mind, Swarm, Niche). It pairs FlyBody/MuJoCo body and small-scene swarm renders with curated empirical BeeBrain datasets and reduced deterministic kernels, keeping fidelity a declared per-module property: every quoted number is traceable from configuration to artifact to manuscript, gaps are catalogued rather than hidden, and the validation rate is a config-band self-test measure, not a biological-realism score. This 1.0 release accompanies the manuscript 'BeeStack: An Evidence-Typed Scaffold for Whole-Colony Honeybee Simulation' and includes the combined PDF and the full source archive. Source: https://github.com/docxology/BeeStack
Overview source: Curated paper metadata.
Methods and contributions
Read the source for the full argument, qualifications, and evidence.
Findings and contributions
- The empirical run integrates 48 response panels, 7 anatomy inventories and 24 odor templates, with a parseable-source fraction of 0.800.
- All module contract self-tests pass (rate 1.000) alongside 11 catalogued open gaps; the authors stress this is contract conformance, not biological validation.
- BeeBody visual scores (0.980 visual, 1.000 silhouette) certify that renders look like a bee, not that kinetics match; masses, adhesion and aerodynamics are FlyBody defaults.
- Strict swarm scenes are small scripted-pose contact scenes (3 scenes, 15 unique contact pairs); colony dynamics use reduced kernels with 50 agents representing 20,000 workers.
- The authors state the results do not show BeeStack can yet predict colony survival, pesticide response, full dance-language use, or field foraging success.
Methods
- Five typed Python modules (Body, Brain, Mind, Swarm, Niche) with contracts — A five-layer honeybee specification is converted into five typed Python modules with explicit contracts, deterministic seeding and a hydrated manuscript.
- BeeBody: FlyBody walking/flight tasks on a generated honeybee MJCF in MuJoCo — Body renders use FlyBody walking and wing-beat flight tasks through a generated honeybee MJCF body plan in MuJoCo; strict swarm scenes use scripted poses with real contact detection.
- BeeBrain ingestion of curated public Apis mellifera datasets — Public anatomy and activity datasets (Honey-Bee Standard Brain, glomerular odor codes, calcium imaging, etc.) are parsed into anatomy inventories, response panels and dance templates.
- Reduced deterministic kernels for BeeMind, BeeSwarm and BeeNiche — Active-inference-style policy scoring, dance recruitment and comb/thermal stepping are implemented as reduced, hand-calibrated deterministic kernels with diagnostics.
- Claim ledger and manuscript hydration from run-time JSON — A claim ledger maps each claim class to evidence and what it does not prove; manuscript numbers are read from pipeline-generated JSON, and tests use no mocks.
Summary sources: Paper metadata and evidence · Extracted source text.
PDF downloads
Archived files available directly from this site.
- BeeStack_combined.pdf PDF · 7.69 MiB · extracted-text source
Citation
Citation metadata follows the unified bibliography.
Catalog details and resources
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