Quantitative classification of harbor seal breeding calls in Georgia Strait, Canada
Bibliographic record
Abstract
During breeding season, male harbor seals (Phoca vitulina) produce underwater calls used in sexual competition and advertisement. Call characteristics vary among populations, and within-population differences are thought to represent individual variation. However, vocalizations have not been described for several populations of this widely-distributed and genetically diverse species. This study describes the vocal repertoire of harbor seals from British Columbia, Canada. Underwater recordings were made near Hornby Island during the summer of 2014 using a single hydrophone. A wide variability was detected in breeding vocalizations within this single breeding site. Four candidate call types were identified, containing six subtypes. Linear discriminant analysis showed 88% agreement with subjective classification of call types, and 74% agreement for call subtypes. Classification tree analysis gave a 92% agreement with candidate call types, with all splits made on the basis of call duration. Differences in duration may have reflected individual differences among seals. This study suggests that the vocal repertoire of harbor seals in this area comprises a vocal continuum rather than discrete call types. Further work with the ability to localize calls may help to determine whether this complexity represents variability due to propagation conditions, animal orientation, or differences among individual seals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".