Estimating Proportional Contributions of Migratory Bull Trout from Hierarchical Populations to Mixed‐Stock Recreational Fisheries Using Genetic and Trapping Data
Bibliographic record
Abstract
Abstract Genetic assignment methods were used to assign 104 adult migrant bull trout Salvelinus confluentus from five recreational fisheries in the Oldman River drainage (Alberta, Canada) to a baseline of three coarse‐scale genetic stocks that had been previously identified with a model‐based Bayesian clustering method. Based on individual assignment and genetic stock identification, most fisheries were largely dominated by the stock with the most proximate spawning tributaries; however, assignment tests suggested variation among source stocks in the proportions of long‐range (>90 river kilometers) migrants relative to short‐range migrants (i.e., that used nearby river systems draining spawning streams). Migrants originating from a subset of the drainage were then subjected to a finer‐scale mixed‐stock analysis in which populations at near‐tributary scales were used as the baseline. These stock proportions were compared with estimates from direct observations and were found to yield similar values to stock proportions derived from 2 years of trapping in several spawning streams. These genetic assignment methods may be used to infer contributions of large and fine‐scale hierarchical populations to mixed‐stock recreational fisheries and are especially applicable for use by inland recreational fisheries managers, which have traditionally not taken advantage of spatial genetic analysis tools to the extent used by coastal commercial fisheries managers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".