Classical individual assignments versus mixture modeling to estimate stock proportions in Atlantic salmon (<i>Salmo salar</i>) catches from DNA microsatellite data
Bibliographic record
Abstract
Mixture modeling is shown to outperform classical individual assignments for both estimating stock composition and identifying individuals' sources in a case study of an eight-locus DNA microsatellite database from 26 Atlantic salmon (Salmo salar) stocks of the Baltic Sea. Performance of the estimation methods was compared using self-assignment tests applied to each of the baseline samples and using independent repeat samples from two of the baseline stocks. The different theoretical underpinnings, hypothesis testing versus decision theory, of the methods explain their estimation capacities. In addition, actual catch samples from three northern Baltic Sea sites in 2000 were analysed by mixture modeling, and estimated compositions were consistent with previous knowledge. Baltic main basin and Gulf of Finland stocks were each minor components (<1% at any site), and three groups of Gulf of Bothnia stocks, wild (36%43% among sites), Finnish hatchery (15%49%), and Swedish hatchery (11%41%), were each important with the two hatchery contributions trending geographically.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".