INDIVIDUALITY IN THE VOICE OF FUR SEAL FEMALES: AN ANALYSIS STUDY OF THE PUP ATTRACTION CALL IN <i>ARCTOCEPHALUS TROPICALIS</i>
Bibliographic record
Abstract
Abstract Like most otariids species, the Subantarctic fur seal breeds on land in large, dense colonies. Pups are confronted by the long and repetitive absences of their mother throughout lactation. At each mother's return, pups have to find her among several hundreds of congeners. This recognition process mainly relies on acoustic signals. We performed an acoustic analysis on 125 calls from 20 females recorded during the 1999–2000 breeding season on Amsterdam Island (Indian Ocean). Ten variables were measured in both temporal and frequency domains. To find the acoustic parameters supporting individual signature, we assessed the differences between individuals using Kruskall‐Wallis univariate analysis of variance. For each variable, we also calculated the potential of individuality coding (PIC) as the ratio between the between‐individual coefficient of variation and the mean value of the within‐individual coefficients of variation. We found that the frequency spectrum, the characteristics of the frequency modulation of the initial and middle part of the call and the call duration exhibit an important individual stereotypy (PIC values ranging between 1.5 and 3), whereas features relative to amplitude and the frequency modulation of the final part of the call are weakly individualized (PIC values between 1 and 1.2).
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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 | 0.001 | 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".