Abundance and distribution of ringed and bearded seals in the Chukchi Sea: a reference for future trends
Bibliographic record
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 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 seal counts to environmental variables, and used the relationships to estimate the species’ distributions and abundance. We accounted for incomplete detection due to: seals missed by sensors or image processing errors; behavioral responses to aircraft; or incomplete availability (seals in the water or in snow dens on the ice, called lairs). To our knowledge, this is the first abundance estimate that accounts for use of lairs by ringed seals. We estimated there were 592 577 (95%, 478 448–733 929) ringed seals and 147 421 (95%, 114 155–190 380) bearded seals in the Chukchi Sea. The influence of remotely sensed environmental variables, such as snow depth and ice type, was consistent with the species’ natural history, particularly ringed seals’ need for snow of adequate depth for lairs. Our study provides the first comprehensive abundance estimates for these seals in a large portion of their ranges 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".