Individual variability in diving, movement and activity patterns of adult bearded seals in Svalbard, Norway
Bibliographic record
Abstract
Abstract Bearded seals are one of the least studied Arctic marine mammals, despite their circumpolar distribution and importance as a resource to Inuit communities. In this study, adult bearded seals (Erignathus barbatus) were equipped with GPS-Argos-CTD-SRDLs in Svalbard, Norway (2011–2012, n = 7) to document their diving, activity and movement patterns in a region where their habitat is changing rapidly. Five seals transmitted for > 8 months, sending 21,738 GPS-positions and 17,866 dives between July and April. The seals spent little time hauled out (≤ 5%). Diving, which occupied 74 ± 3% of their time, was generally shallow (24 ± 7 m, max: 391 m) and of short duration (6.6 ± 1.5 min, max: 24 min) with deeper, longer dives in winter/spring compared to summer. All seals occupied shallow, coastal areas and relatively small 50% home ranges (10–32 km2). However, individuals exhibited high degrees of specialization in their habitat use and diving behaviour, differing markedly with respect to proportions of benthic vs pelagic dives (range: 51–95% benthic dives), distance to glacier fronts (range: 3–22 km) and in the time spent at the bottom of dives (range: 43–77%). Having specialized strategies within a generalist population may help bearded seals adapt in a rapidly changing Arctic ecosystem.
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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".