The ontogeny of diving behaviour in New Zealand fur seal pups (<i>Arctocephalus forsteri</i>)
Bibliographic record
Abstract
This study investigated the development of diving in 21 New Zealand fur seal pups, Arctocephalus forsteri (Lesson, 1828), prior to weaning at Cape Gantheaume, Kangaroo Island. Diving behaviour was examined using time–depth recorders, which were deployed during two time periods, 5 months prior to weaning (n = 6) and 2 months prior to weaning (n = 15). Scats were also examined to assess whether fur seal pups foraged prior to weaning. The maximum dive depth attained was 44 m, while the maximum dive duration was 3.3 min. Immediately prior to weaning, fur seal pups spent a greater proportion of their time diving at night, and concomitantly several measures of diving performance also increased. In general, pups dived successively deeper (6–44 m between June and September), and the average number of dives per day, dive frequency, and vertical distance travelled increased. Prey remains were present in approximately 30% of scats and indicated that some pups were foraging as early as June (5–6 months of age, approximately 4–5 months prior to weaning). Of the scats that contained prey remains, fish (South American pilchard, Sardinops sagax (Jenyns, 1842); Australian anchovy, Engraulis australis (White, 1790); and redbait, Emmelichthys nitidus Richardson, 1845) accounted for 43% of the prey items found, crustaceans accounted for 36%, and cephalopods (Gould's squid, Nototodarus gouldi (McCoy, 1888)) accounted for 20%.
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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".