Identifying the contribution of wild and hatchery Chinook salmon (<i>Oncorhynchus tshawytscha</i>) to the ocean fishery using otolith microstructure as natural tags
Bibliographic record
Abstract
Quantifying the contribution of wild (naturally spawned) and hatchery Chinook salmon (Oncorhynchus tshawytscha) to the mixed-stock ocean fishery is critical to understanding their relative importance to the persistence of salmon stocks. The inability to distinguish hatchery and wild salmon has inhibited the detection of declines or recoveries for many wild populations. By using Chinook salmon of known hatchery and wild origin, we established a baseline for separating these two sources using otolith microstructure. Otoliths of wild salmon contained a distinct exogenous feeding check likely reflecting an abrupt transition in food resources from maternal yolk not experienced by fish reared in hatcheries. Daily growth increments in otoliths from hatchery salmon immediately after the onset of exogenous feeding were wider and more uniform in width than those in wild fish. The discriminant function that we used to distinguish individuals reared in hatcheries or in the wild was robust between years (1999 and 2002), life history stages (juveniles and adults), and geographic regions (California, British Columbia, and Alaska) and classified fish with ~91% accuracy. Results from our mixed-stock model estimated that the contribution of wild fish was 10% ± 6%, indicating hatchery supplementation may be playing a larger role in supporting the central California coastal fishery than previously assumed.
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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 | 0.001 | 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) | 0.001 | 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".