Investigating high seas foraging conditions for salmon in the North Pacific: insights from a 100-year scale archive for Rivers Inlet sockeye salmon
Bibliographic record
Abstract
The high seas phase of Pacific salmon life history remains particularly data-limited, and the potential implications of climate impacts on ocean productivity for salmon condition and reproductive success is poorly understood. We used carbon and nitrogen stable isotopes (SI) from salmon scales to reconstruct aspects of the marine environment experienced by Rivers Inlet sockeye salmon (Oncorhynchus nerka) over the last century (1915–2016). Time series of SI compositions of salmon scales showed a greater variability after 1950, probably linked to more dynamic high seas environmental conditions. However, climate indices (e.g., Pacific Decadal Oscillation, El Niño Southern Oscillation) did not explain the SI variability. We assessed the time series with respect to changes in food web dynamics, including shifting trophic baselines, changes in prey–salmon diet, and changes in salmon foraging location. A significant correlation (r = 0.53) between sea surface temperature and δ13C enabled us to define the area of potential salmon distribution in the open ocean for approximately 9 months prior to sampling. This method shows high potential for stock-specific high seas distribution mapping that could be combined with prey isotope values to inform stock-specific foraging experience.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".