Molecular pedigree analysis in natural populations of fishes: approaches, applications, and practical considerations
Bibliographic record
Abstract
Molecular markers can provide information on the family structure of natural fish populations through molecular pedigree analysis. This information, which is otherwise difficult to obtain, can give important insights into the expression and evolution of phenotypic traits. We review the literature to provide examples of how molecular pedigree analysis has been used extensively to examine patterns of distribution, dispersal, and social behaviour in fishes and how it provides a tool for the estimation of quantitative genetic parameters. Although multiple methodologies can be used to examine family structure, the efficacy of any molecular pedigree analysis is generally dependent on prior consideration of interrelated statistical and biological factors. Statistical issues stem from the choice of molecular marker type and marker set used, in addition to sampling strategy. We discuss these considerations and additionally emphasize the utility of supplemental nongenetic data for increasing the efficacy of pedigree analysis. We advocate that, where possible, a priori knowledge of the study system's biology should be used to inform study design and further highlight the need for additional empirical testing of methodologies.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".