Climate effects on size‐at‐age and growth rate of Chinook Salmon (<i>Oncorhynchus tshawytscha</i>) in the Fraser River, Canada
Bibliographic record
Abstract
Abstract Decline in size‐at‐age of Chinook Salmon (Oncorhynchus tshawytscha) has been observed for many populations across the entire Northeast Pacific Ocean, and identifying external drivers of this decline is important for sustainable management of these ecologically, economically, and culturally valuable resources. We assessed size‐at‐age of 96,939 Chinook Salmon sampled in the Fraser River watershed (Canada) from 1969 to 2017. A broad decline in size‐at‐age was confirmed across all population aggregates of Fraser River Chinook Salmon, in particular since year 2000. By developing a novel probability‐based approach to calculate age‐ and year‐specific growth rates for Fraser River Chinook Salmon and relating growth rates to environmental conditions in specific years through a machine learning method (boosted regression trees), we were able to disentangle multi‐year effects on size‐at‐age and thus identify environmental factors that were most related to the observed size‐at‐age of Chinook Salmon. Among 10 selected environmental variables, ocean salinity at Entrance Island in spring, the Aleutian Low Pressure Index and the North Pacific Current Bifurcation Index were consistently identified as important contributors for four of the seven age and population aggregate combinations. These top environmental contributors could be incorporated into future stock assessment and forecast models to improve Chinook Salmon fisheries management under climate change.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".