Abundance and distribution of ringed and bearded seals in the Chukchi Sea: a reference for future trends
Bibliographic record
Abstract
Abstract Ringed ( Pusa hispida ) and bearded ( Erignathus barbatus ) seals are vulnerable to decreasing sea ice habitat in the rapidly warming Arctic. In April and May of 2016, we conducted an aerial survey over the ice-covered areas of the Chukchi Sea using thermal and color cameras to detect and count these seals on sea ice. We related the seal counts to environmental variables, and used the relationships to estimate the species’ distributions and abundance throughout the Chukchi Sea. We accounted for incomplete detection due to seals missed by sensors or image processing errors, behavioral responses to aircraft, or incomplete availability (i.e. seals that are in water or in snow dens on the ice, called lairs). For the latter, we used wet/dry records from satellite-linked bio-loggers, and remotely sensed snow melt dates to estimate the proportion of ringed seal individuals that were visible on ice for each day and location surveyed. To our knowledge, this is the first study where use of lairs by ringed seals has been formally addressed while estimating abundance from aerial surveys. Ringed seal abundance was estimated as = 592,577 (95% CI: 478,448–733,929), with highest densities near Kotzebue Sound, Alaska, USA. Bearded seal abundance was estimated as = 147,421 (95% CI: 114,155–190,380), with highest densities in broken pack ice near Bering Strait. The influence of environmental variables, such as snow depth and ice type, was consistent with prior studies of the species’ natural history, particularly ringed seals’ preference for snow of adequate depth for lairs. Our study provides the first comprehensive abundance estimates for ringed and bearded seals in the Chukchi Sea and establishes a reference for monitoring how their populations respond to Arctic warming.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".