Probabilistic-based genetic assignment model: assignments to subcontinent of origin of the West Greenland Atlantic salmon harvest
Bibliographic record
Abstract
Abstract Sheehan, T. F., Legault, C. M., King, T. L., and Spidle, A. P. 2010. Probabilistic-based genetic assignment model: assignments to subcontinent of origin of the West Greenland Atlantic salmon harvest. – ICES Journal of Marine Science, 67: 537–550. A multistock Atlantic salmon (Salmo salar) fishery operates off the coast of West Greenland and harvests fish of North American and European origin. Annual landings peaked in 1971 at 2700 t, but declined to 22 t in 2003. Biological data are collected to characterize the catch and its stock composition. Multilocus genotypes, generated via microsatellite DNA analysis, are used to derive statistics on continent of origin and less accurate finer-scale assignments. We developed a probabilistic-based genetic assignment (PGA) model to estimate the contribution of salmon from individual North American rivers in the 2000–2003 West Greenland catch. Uncertainty associated with finer-scale assignments is addressed by incorporating estimated misclassification rates and by reporting results as distributions generated via Monte Carlo resampling. US-origin fish represented ∼1% (by number) of the salmon harvested at West Greenland during the years 2000–2003. The resulting loss of spawners to this stock complex was approximately half the estimated adult returns in 2001, but was below 4% in the other 3 years. This is the first attempt to partition the US component of the West Greenland mixed-stock fishery to its finer parts. The approach can be used to identify the effects of fishing on individual stocks within any multistock complex where genetic samples of known origin are 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".