Individual signatures in the frequency‐modulated sweep calls of African large‐eared, free‐tailed bats<i>Otomops martiensseni</i>(Chiroptera: Molossidae)
Bibliographic record
Abstract
Abstract Frequency‐modulated sweep calls ofOtomops martiensseniwere recorded from individuals as they emerged from nine different building roosts near Durban, South Africa. Multiple analyses of call features, including duration (ms), lowest frequency (kHz), highest frequency (kHz) and frequency with most energy (kHz), indicated significant inter‐individual variation. Discriminant function analysis of call features correctly classified the calls of individuals from four roosts > 70% on 19 of 28 times. Although other species of molossids (Chaerephon pumilus,Tadarida aegyptiaca, and one unidentified species) produced social calls as well as frequency‐modulated sweep calls,O. martiensseniproduced just the latter vocalizations and they were longer and lower in frequency than those of the sympatric molossids. Other species of molossids, but notO. martiensseni, produced feeding buzzes as they attacked flying insects. The frequency‐modulated sweep calls ofO. martiensseniseem to serve a communication function, but they may not be used in echolocation unlike similar calls by other molossids. Individually distinct communication signals (frequency‐modulated sweep calls) enhance communication in a species that lives in year‐round social groups (one adult male, females and dependent young).
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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".