Influences of ocean conditions and feeding ecology on the survival of juvenile Chinook Salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Abstract Recruitment variability in many fish populations is postulated to be influenced by climatic and oceanographic variability. However, a mechanistic understanding of the influence of specific variables on recruitment is generally lacking. Feeding ecology is one possible mechanism that more directly links ocean conditions and recruitment. We test this mechanism using juvenile Chinook Salmon ( Oncorhynchus tshawytscha ) collected off the west coast of Vancouver Island, British Columbia, Canada, in 2000–2009. Stable isotopes of carbon ( δ 13 C), an indicator of temperature or primary productivity, and nitrogen ( δ 15 N), an indicator of trophic position, were taken from muscle tissues of genetically stock‐identified salmon. We also collated large‐scale climate indices (e.g., Pacific Decadal Oscillation, North Pacific Gyre Oscillation), local climate variables (e.g., sea surface temperature) and copepod community composition across these years. We used a Bayesian network to determine how ocean conditions influenced feeding ecology, and subsequent survival rates. We found that smolt survival of Chinook Salmon is predicted by their δ 13 C value, but not their δ 15 N. In turn, large‐scale climate variability determined the δ 13 C values of salmon, thus linking climate to survival through feeding ecology, likely through qualities propagated from the base of the food chain.
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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 | 0.000 | 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".