Differences between mates at the TLR1Lb locus are associated with lower reproductive success in a long-lived seabird
Bibliographic record
Abstract
Genetic diversity can influence fitness components such as survival and reproductive success. Yet the association between genetic diversity and fitness based on neutral loci is sometime very weak and inconsistent, with relationships varying among taxa due to confounding effects of population demography and life history. Fitness-diversity relationships are likely to be stronger and more consistent for genes known to influence phenotypic traits, such as immunity-related genes, and may also depend on the genetic differences between breeding partners. We recorded breeding success of individuals and breeding pairs over 20 years to evaluate the relationships between reproductive success and both neutral genetic variation (using 7,830 single nucleotide polymorphisms) and functional variation (four toll-like receptor [TLRs] loci) with reproductive success in thick-billed murres (Uria lomvia). Individual genetic diversity (both neutral and functional) was unrelated to reproductive success, but surprisingly, successful multi-year reproductive success decreased with the genetic difference between breeding partners at TLR1Lb. This result may be due to an advantage of specific alleles at TLR1Lb. This study is one of few addressing both individual genetic variation and genetic similarity between mates at both neutral and functional variation in a long-lived bird.
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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".