EFFECTS OF CLUTTER ON ECHOLOCATION CALL STRUCTURE OF MYOTIS SEPTENTRIONALIS AND M. LUCIFUGUS
Bibliographic record
Abstract
The structure of echolocation calls, and the distance over which bats perceive their environment, varies with the amount of structural clutter through which they are flying. Clutter and species had significant effects on the frequency-time characteristics of search-phase echolocation calls of northern long-eared (Myotis septentrionalis) and little brown bats (M. lucifugus). We tested an a priori derived model that predicted the pattern of differences in echolocation call variable values among clutter categories would provide insight into the relative maximum distances that bat species could perceive using echolocation. Specifically, the model predicted that species adapted to flying and foraging in cluttered habitats would have a shorter maximum perceptual distance than species adapted to flying and foraging in uncluttered habitats. The results supported this model and suggest the clutter-adapted M. septentrionalis had a shorter maximum perceptual distance than M. lucifugus, a species known to forage in a variety of habitats but mainly in uncluttered areas (i.e., over water). Using calls as the sampling unit, a neural network correctly classified >94% of the echolocation calls to species in high clutter. In medium and low clutter, >82% of the calls were correctly classified to species; however >90% correct classification was achieved by leaving >30% of calls unclassified. Researchers should develop clutter-specific call libraries to improve species classification accuracy for echolocation calls.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".