Pore plate sensilla scale and distribution modulate odor capture around honey bee antennae
Bibliographic record
Abstract
Insects increase fitness by extracting chemical odor information from the environment using chemoreceptors on antennae that exhibit diverse morphology across species. Honey bees have rod-like antennae with three segments. The most distal segment is the flagellum, which includes pore plates with olfactory receptor neuron dendrites that contain odor receptors. Many studies have examined flow and odor dynamics around antennal structures with simplified sensillae; however, no extant studies resolve pore plate scale fluid processes along the antennal surface. Here, we numerically modeled honey bee antennae in odorized flow fields to evaluate effects of pore plate scale and distribution on fluid drag forces and odor capture rates. For a range of wind speeds and pore plate sizes, we find that boundary layer development enhances odor fluxes preferentially to leading edge pore plate sensilla. This could explain observations of increased pore plate densities along the leading edge of honey bee antennae. Furthermore, we find an asymptotic limit on odor capture rates for a fixed antennal size for dense pore plate sensilla; these pore plates ultimately compete for a finite supply of odor. Larger antennae capture more odor but do so at an energetic cost due to increased drag forces. Our findings can guide both bio-inspired design principles of robotic olfaction and experimental efforts aimed at studying insect odor perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".