Relationships between heterozygosity, allelic distance (<i>d</i><sup>2</sup>), and reproductive traits in chinook salmon,<i>Oncorhynchus tshawytscha</i>
Bibliographic record
Abstract
Correlations of various measures of individual genetic variation with fitness have been reported in a number of taxa; however, the genetic nature of such correlations remains uncertain. To explore this, we mated 100 male and 100 female chinook salmon (Oncorhynchus tshawytscha) in a one-to-one breeding design and quantified reproductive fitness and allocation (male gonadosomatic index, GSI; female fecundity; egg size; egg survival). Each fish was scored for allele size at seven microsatellite loci. We applied univariate and multivariate regression models incorporating two genetic variation statistics (microsatellite heterozygosity and squared allelic distance, d2) with reproductive parameters. The majority of the relationships were found to be nonsignificant; however, we found significant, positive, univariate relationships for fecundity and GSI (25% of tests) and significant, multivariate relationships at individual loci for all four traits (13% of tests). One microsatellite locus, Omy207, appeared to be closely associated with reproductive fitness in female chinook salmon (but not male), based on the multivariate analysis. Although direct tests for overdominance versus inbreeding effects proved inconclusive, our data are consistent with the presence of both inbreeding (general) and overdominance (local) effects on reproductive traits in chinook salmon.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".