Bayesian inference from the conditional genetic stock identification model
Bibliographic record
Abstract
Genetic stock identification (GSI) estimates stock proportions and individual assignments through comparison of genetic markers with reference populations. It is used widely in anadromous fisheries to estimate the impact of oceanic harvest on riverine populations. Here, we provide a formal, explicit description of Bayesian inference in the conditional GSI model, documenting an approach that has been widely used in the last 5 years, but not formally described until now. Subsequently, we describe a novel cross-validation method that permits accurate prediction of GSI accuracy when making Bayesian inference from the conditional GSI model. We use cross-validation and simulation of genetic data to confirm the occurrence of a bias in reporting-unit proportions recently reported in Hasselman et al. (2016) . Then, we introduce a novel parametric bootstrap approach to reduce this bias, and we demonstrate the efficacy of our correction. Our methods have been implemented as a user-friendly R package, rubias, which makes use of Rcpp for computational efficiency. We predict rubias will be widely useful for GSI of fish populations.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".