Influence of immune-relevant genes on mate choice and reproductive success in wild-spawning hatchery-reared and wild-born coho salmon (<i>Oncorhynchus kisutch</i>)
Bibliographic record
Abstract
Hatcheries support fisheries and aid in the recovery of endangered wild populations. Evidence for reduced reproductive success (RS) in wild-spawning hatchery-reared salmon compared with that in wild-born fish invites questions about the impact on subsequent generations. Immune gene-dependent mate preference is one mechanism known to influence salmonid fitness. We evaluated mate choice and correlates of RS to better understand fitness differences between hatchery-reared and wild-born fish using a previously constructed genetic pedigree of coho salmon (Onchorhynchus kisutch) from the Umpqua River, Oregon. Two years (2005 and 2006) of three wild-spawning mate pair classes were examined: wild × wild (W × W), hatchery × hatchery (H × H), and wild × hatchery (W × H). We found no evidence for mate choice within mate pair classes based on microsatellites linked to the major histocompatibility complex (MHC) and immune-relevant expressed sequence tags. Greater W × W mate pair RS was associated with increased MHC diversity in 2005 and 2006, while greater W × H mate pair RS was correlated to intermediate MHC diversity in 2006. We found no correlation between MHC diversity and H × H mate pair RS. Our results suggest that greater MHC diversity between wild-born coho pairs may increase offspring survival.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".