Indexing recruitment for source populations contributing to mixed fisheries by incorporating age in genetic stock identification models
Bibliographic record
Abstract
We describe a methodology for estimating relative recruitments for source populations (sources) contributing to mixed fisheries by incorporating age into genetic stock identification models. The approach produced recruitment estimates that were strongly correlated (median correlation = 0.849; 2.5 and 97.5 percentile in correlations = 0.613 and 0.951, respectively) with simulated recruitments across various design factors, including number of sources, genetic divergence among sources, and temporal variation in source recruitments. Sensitivity analyses indicated that the approach was robust to aging inaccuracies and assumed source mortalities. Application to walleye (Sander vitreus) sources contributing to the Saginaw Bay, Lake Huron, fishery produced similar recruitment estimates to assessment models. There was greater discrepancy between recruitment estimates for lake trout (Salvelinus namaycush) hatchery strains in northern Lake Michigan when compared with strain stocking levels, although this mismatch may stem from stocking levels being a poor recruitment measure. The estimation approach should prove beneficial for indexing source recruitment based on fishery or assessment collections from mixtures, even when long-term time series of harvest and survey data required for integrated assessments are not available.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".