Spatial‐temporal patterns in intra‐annual gray whale foraging: Characterizing interactions between predators and prey in Clayquot Sound, British Columbia, Canada
Bibliographic record
Abstract
Abstract In Clayquot Sound, British Columbia, gray whales (Eschrichtius robustus) forage primarily on mysids (FamilyMysideae) and also on crab larvae (FamilyPorcellanidae) that are constrained to specific habitat, which relate to bathymetric depths. In this paper we characterize the interactions of gray whales and their prey by analyzing fine scale spatial‐temporal patterns in foraging gray whale distribution within a season. Kernel density estimators are applied to two seasons (1998 and 2002) of high‐resolution data on foraging by gray whales. By partitioning data from each foraging season into several time periods (12 in 1998 and 11 in 2002), using a temporal autocorrelation function, and generating kernel density estimated surfaces for each time period, it is possible to identify discrete areas of increasing and declining foraging effort. Our results indicate that gray whales forage on mysids throughout a season and opportunistically forage on crab larvae. The episodic crab larvae feeding may reduce, but not eliminate, pressure to mysid populations enabling mysids to reassemble swarms and continue to support gray whale foraging in the latter part of the season. Results suggest that when managing marine environments, gray whale populations require multiple and connected habitats for summer foraging.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".