Marine biophysical conditions influence the vertical and horizontal distribution of sub-adult Chinook salmon in nearshore marine waters
Bibliographic record
Abstract
The present study quantified the vertical and horizontal distribution of sub-adult Chinook salmon Oncorhynchus tshawytscha in nearshore marine environments. Depth-specific hook and line sampling was conducted along the Pacific Ocean coast of Washington, USA. Our analysis, based on 187 Chinook salmon from 1299 sampling deployments and 6616 hooks, revealed a wide distribution of salmon in nearshore marine waters, with distinct patterns associated with fish size and age. Chinook salmon that spent one winter in marine waters were more likely to be caught at greater depths than those in their first year at sea, and larger fish were found at greater depths than smaller fish. The probability of Chinook salmon capture varied with depth, showing a higher likelihood of capture at midwater (>15 m from the surface and >5 m from the bottom) and near (<5 m) the bottom compared to near (<15 m) the surface. Additionally, environmental variables such as sea surface temperature, sea surface chlorophyll a, minutes to low tide, and boat speed unimodally influenced capture probability. Our study contributes valuable insights into the spatiotemporal ecology of Chinook salmon, offering a more mechanistic perspective for their management and conservation. The identified relationships between environmental covariates and Chinook salmon distribution can be used to inform life cycle models used to manage and protect this at-risk species and the ecosystem processes that depend on them, particularly in the context of changing oceanic conditions and their role as both predator and prey in marine ecosystems.
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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 | 0.000 | 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".