Genetic and Coded Wire Tag Results Combine to Allow More-Precise Management of a Complex Chinook Salmon Aggregate
Bibliographic record
Abstract
Abstract Conservation concerns for small, relatively unproductive populations of Chinook salmon Oncorhynchus tshawytscha limit the utility of fisheries in Canada's Fraser River. To identify population-specific migration time and to index abundance, we analyzed 4,822 fish sampled for genetic variation in 2000 and 2001 and 580 fish with coded wire tags (CWTs) caught from 1987 to 2004 in a test fishery near the river mouth. Population sizes estimated from microsatellite variation were within 3.4% of the known-origin population composition and were unbiased in comparison with known-origin population sizes. All but 1 of the 30 populations detected by both genetic methods and CWTs had overlapping migration times, but these times differed significantly for only 7 populations. Migration times were identified for another 23 untagged populations identified by using genetics, which resulted in the assignment of migration timing groups (peak passage) for 53 populations as spring (March–May), early summer (June), midsummer (July), late summer (August), and fall (September–October). Population abundance indices at the test fishery were significantly associated with run size at the river mouth. When populations were aggregated by geographic stock structure and migration time, the abundance indices for the test fishery explained 80% of the variation in run size. Incorporating genetic information can substantially improve the utility of test fishery data and thereby allow more-precise management of complex population aggregates such as those in the Fraser River.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".