Temporal Variability in Call Rates of Weddell ( <scp> <i>Leptonychotes weddellii</i> </scp> ) and Leopard ( <scp> <i>Hydrurga leptonyx</i> </scp> ) Seals Near Davis Station, East Antarctica
Bibliographic record
Abstract
ABSTRACT We investigated Weddell seal ( Leptonychotes weddellii ) and leopard seal ( Hydrurga leptonyx ) vocalizations using passive acoustic monitoring beneath landfast ice and open water, 5.6 km seaward from Davis Station, East Antarctica. Eight‐minute recordings were manually sampled at 1 h intervals over 24 h every 10 days from 24 July 2021 to 30 January 2022. Call counts for Weddell seals were highest in the winter, lower during the breeding season, and almost absent in the summer. This change coincided with the occurrence of open water over the recording site, a progressive shift in Weddell seal behavior from foraging to aggregating in local fjords for the annual pupping, mating, and molting periods, and an increase in leopard seal—a potential predator—call detection rates. Leopard seal calls were primarily detected in December after the landfast ice broke up. In winter, Weddell seal calling counts were highest at dusk and night, and lowest during the day when the seals may have been feeding. Neither species exhibited a diel calling pattern once there were 24 h of sunlight. Seal locations were associated with the presence and absence of landfast ice, and call detections were influenced by their distances to the recorder.
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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".