Temporal patterns and ecosystem correlates of chum salmon (<i>Oncorhynchus keta</i>) migration phenology in the Pacific Northwest
Bibliographic record
Abstract
Understanding and quantifying migration phenology of commercially harvested Pacific salmon (Oncorhynchus spp.) is a cornerstone for managing sustainable populations. Here, we use a multidecadal data time series together with a hypothesis-driven framework to evaluate migration phenology in adult fall and winter ecotype chum salmon (O. keta) in a poorly studied but highly managed system — the South Puget Sound (SPS) of Washington State, USA. Using generalized additive mixed models that accounted for temporal autoregressive dynamics, we examined the effect of commercial harvest, climate variation, intraspecific density dependence, and predator buffering on migration timing and run duration. SPS chum salmon are migrating earlier over time, especially the winter ecotype that showed the strongest temporal shift from historical timing. Migration timing shifts were closely associated with regional marine climate regimes, local scale freshwater availability, and increasing pinniped abundance. We conclude that there is potential for the winter ecotype migration converging with that of the fall ecotype and that directional change in migration phenology may be driven by a unique combination of ecosystem factors.
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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) | 0.000 | 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".