Prey biomass dynamics in gray whale feeding areas adjacent to northeastern Sakhalin (the Sea of Okhotsk), Russia, 2001–2015
Bibliographic record
Abstract
Changing climate patterns strongly influence marine ecosystems across the Pacific Arctic region creating significant ecosystem transitions and change. Macrobenthic species are essential prey for numerous marine mammals and seabirds but the influence of climatic drivers that control macrobenthic community population dynamics are poorly known in critical prey habitats. We investigated associations of environmental, temporal, and climatic covariates with the biomass concentrations of six prey groups (Actinopterygii, Amphipoda, Bivalvia, Cumacea, Isopoda, and Polychaeta) in essential habitats for Korean-Okhotsk (western) gray whales adjacent to northeastern Sakhalin Island in the Sea of Okhotsk. Prey community biomass concentrations were correlated with water depth, year, and climate indices reflecting oceanographic and climatic patterns associated with macro-scale climatological effects. The correlation of prey biomass with water depth and year accounted for ∼90% of total variation in canonical correlation analyses (CCor). Climate indices accounted for ∼10% of total variation in CCor. Water circulation in winter may be particularly important for maintaining populations through the advection of particulate organic carbon entrained in winter currents. Overall, temporal trends in the biomass concentrations of gray whale prey resources appear to reflect climatic and oceanographic factors that are driving ecosystem changes across the Sea of Okhotsk and the Pacific Arctic region.
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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 | 0.001 | 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".