Body condition of gray whales (Eschrichtius robustus) feeding on the Pacific Coast reflects local and basin-wide environmental drivers
Bibliographic record
Abstract
A small subset of the Eastern North Pacific gray whale population does not make the full migration from wintering grounds in Mexico to feeding grounds in the Bering, Chukchi and Beaufort seas and instead feed along the Pacific Coast between northern California and northern British Columbia – this group is known as the Pacific Coast Feeding Group (PCFG). We evaluated the body condition of PCFG whales observed in northern Washington and along Vancouver Island to evaluate how body condition of gray whales changes within and between years. We found that PCFG gray whales improve body condition through the feeding season and at varying rates by year and that they have variability in their body condition at the start and end of each feeding season. The inclusion of environmental factors, particularly the Pacific Decadal Oscillation (lagged two years) and September kelp canopy cover along the Washington coast (lagged one year), drastically improved the ability of a multiple regression model to predict average whale body condition for a given year as compared to models without environmental factors included. A comparison of our findings to a previously published study on body condition of gray whales at Sakhalin Island, Russia highlight the differences of life history strategy between a group of whales with a long migration (Sakhalin whales) and those with a short migration. Whales feeding at Sakhalin Island gain body condition quicker and more predictably to a good body condition by the end of the feeding season than the whales we studied in the PCFG. Photogrammetry may be an effective method for monitoring the effects of climate change on PCFG gray whales.
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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".