Otolith shape as a classification tool for Chinook salmon (<i>Oncorhynchus</i> <i>tshawytscha</i>) discrimination in native and introduced systems
Bibliographic record
Abstract
Chinook salmon (Oncorhynchus tshawytscha) are widely distributed across the globe, with native stocks in the North Pacific Ocean and self-sustained populations in both the Northern and Southern hemispheres. In their native range, Chinook salmon face many conservation and management challenges, including depleted stocks, loss of genetic diversity, and hatchery influences, whereas naturalized range expansion poses a threat to novel ecosystems. Therefore, ways to improve stock discrimination would be a useful tool for fishery managers. Here, we evaluated otolith shape variation in Chinook salmon as a potential tool for stock discrimination using wavelet coefficients and Fourier harmonics in three case studies at multiple spatial scales. We adopted a simple Classification Tree model that used otolith shape variation to separate Chinook salmon groups. We found best performance of the model occurring between hemispheres, followed by Oregon basins, within-watershed Elk River, Oregon, and lastly among South American basins. Otolith shape analysis is a promising tool for stock discrimination if used in conjunction with other methods to better understand plasticity of anadromous species that use pan-environmental systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".