Linking northern fur seal dive behavior to environmental variables in the eastern Bering Sea
Bibliographic record
Abstract
Northern fur seals (Callorhinus ursinus) breeding on the Pribilof Islands, Alaska have declined dramatically over the past 40 years. Effective conservation of northern fur seals depends on understanding the foraging behavior of adult females whose foraging success is linked to pup survival. We determined the foraging behavior for 11 tagged lactating female northern fur seals from the Pribilof Islands using a state‐space modeling approach with an autoregressive movement model. To interpret at‐sea behavior in the context of oceanic habitat, we spatially and temporally matched high‐resolution reconstructed tracks to a set of environmental covariates that included: commercial groundfish catch, sea surface temperature, primary productivity, wind speed, depth and time of day. We used a Bayesian hierarchical framework to implement a multinomial regression model to link behavior to environmental covariates and account for the mismatch of scale between fur seal behavior and the environmental variables by incorporating an error‐in‐covariates approach into the hierarchical model. The Bayesian framework allowed us to build a single model to synthesize the information from all the northern fur seal foraging tracks and the available information about the underlying environmental conditions. Application of the approach indicated that the behavioral states for the northern fur seal were significantly related to the Alaska commercial groundfish catch, particularly walleye pollock (Gadus chalogramma).
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".