Proportional underwater call type usage by Weddell seals (<i>Leptonychotes weddellii</i>) in breeding and nonbreeding situations
Bibliographic record
Abstract
Proportional underwater call type usage by Weddell seals ( Leptonychotes weddellii (Lesson, 1826)) near Mawson, Antarctica, investigated the hypothesis that certain call types function specifically in breeding behaviour. Recordings were collected at various sites in 2000 and 2002 from June to December. Twenty-four hour recordings were collected in 2002 at two sites. One hundred consecutive calls from each of 248 recordings were classified into one of ten common call types. Time to 100 calls provided the calling rate. The study period was divided into four periods representing initial sea-ice formation, pre-pupping, pupping, and mating. Calling rate and light–dark differences were also examined. No presence–absence differences were observed for any of the call types with season. The largest difference between nonbreeding and breeding situations was an increase from 32.2% to 38.1% for descending whistles (F[3,244] = 4.483, p = 0.004). Trills gradually increased from 1.8% to 7.3% toward the mating period (F[3,244] = 30.932, p < 0.001). The proportion of trills, chugs, descending whistles, and other call types also varied with calling rate and light–dark conditions. Some pre-reproductive behaviours may occur in winter, but no call types of Weddell seals function solely in the breeding season.
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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 | 0.001 | 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".