Integrated stock mixture analysis for continous and categorical data, with application to genetic–otolith combinations
Bibliographic record
Abstract
Fish populations or stocks often intermix on fishing grounds, thus posing problems for stock assessors or managers attempting to optimize yields and minimize overexploitation of individual stocks. A Bayesian framework is developed here to simultaneously analyse many of the different data types (e.g., otolith elemental composition, nuclear and mitochondrial DNA) that have been used to identify stock origins of fish in mixed groups and thus take maximal advantage of the available information. Elements of this framework include the capability to analyse each data type either separately or in combination for any number of mixed-group samples, Bayesian credible intervals to evaluate the uncertainty associated with the estimated proportion of the original stocks in the mixed groups, and posterior predictive diagnostics to evaluate the assumptions of the underlying models. The framework was used to re-analyse a subset of otolith elemental composition and microsatellite allele frequency data obtained from the same fish from Atlantic cod ( Gadus morhua ) stocks in the Gulf of St. Lawrence, Canada.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".