Bibliographic record
Abstract
In this paper we examine morphometric attributes of the airframes of 24 species of bat from Western Australia. In particular, we consider anatomical features of the ear, head, body and tail related to lift and drag optimisation as well as airflow separation control. We provide an assessment of the relative cleanliness of the species and a range of lift and drag coefficient values for use in metabolic power output modelling. The species assessed have aerodynamic cleanliness optimisations that are appropriate to the range of Reynolds’ numbers in which bats fly. Head/body relative cleanliness was consistent with, and functionally appropriate to, aspects of species foraging niche such as foraging strategy. Cleanliness of face and fineness ratio of head and body were found to be related to minimum foraging drag. Blending of the wing and body, the presence of a wing/body fillet and the texture of the pelage were found to be important. The aerodynamic optimisation of ears and tail membrane were found to correlate with foraging strategy. The interceptors had optimisations for minimum drag generation consistent with their higher foraging flight speed. Rather than being optimised for minimum drag, the air-superiority bats’ tails and ears were consistent with their highly agile but slower-foraging flight speeds. Surface bats were characterised by the absence of optimisations for low drag. The frugivore plus the nectarivore and the carnivore studied appear to be discrete optimisations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".