Relationships between oceanic conditions and growth of Chinook salmon (<i>Oncorhynchus tshawytscha)</i> from California, Washington, and Alaska, USA
Bibliographic record
Abstract
Abstract We model age‐specific growth rates of Chinook salmon ( Oncorhynchus tshawytscha ) with two life‐history behaviors from Alaska (i. Situk and ii.Taku Rivers), Puget Sound, Washington (iii., iv. Skagit River), and California (v. Smith River) relative to oceanic conditions in those regions. By analyzing over 20 yr of biological and physical data from the NE Pacific downwelling, upwelling, and transition zones, we are able to determine the factors affecting growth across much of the species’ range and between life‐history behaviors. With scale increment data from returning fish, we use path analysis and partial least squares regression to quantify the relationships between growth and regional‐ and large‐scale oceanic conditions (e.g., sea level height, sea surface temperature, upwelling). Alaskan fish with both ocean‐ and stream‐type behaviors were fit best by the environmental data from the winter in Alaska waters. Specifically, coastal and gyre factors such as sea surface temperature, river flow, and Ekman pumping positively correlated to growth, indicating a productive and strong Alaska Current promoted growth. Growth of fish from California was fit by local factors such as increased upwelling, lower coastal sea surface temperature, and wind stresses during summer and spring, indicating a productive and strong California Current promoted growth. For Puget Sound, Washington, growth of fish that migrate to sea in their first year was generally negatively correlated to a strong California Current. Puget Sound fish that spend a year in freshwater before migrating to sea were modeled well with environmental data from their source region for the first 2 yr at sea and by data from Alaska waters in their third year at sea. Results suggest that conditions in which the transition zone is dominated by neither the Alaska nor California Currents are best for increased growth of Puget Sound fish.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".