Inbreeding coefficient and heterozygosity-fitness correlations in unhatched and hatched song sparrow nestmates
Bibliographic record
Abstract
Heterozygosity-fitness correlations use molecular measures of heterozygosity as proxy estimates of individual inbreeding coefficients (f) to examine relationships between inbreeding and fitness traits. Heterozygosity-fitness correlations partly depend on the assumption that individual heterozygosity and f are strongly and negatively correlated. Although theory predicts that this relationship will be strongest when mean f and variance in f are high, few studies of heterozygosity-fitness correlations include estimates of f based on pedigrees, which allow for more thorough examinations of the relationship between f, heterozygosity and fitness in nature. We examined relationships between pedigree-based estimates of f, multilocus heterozygosity (MLH) and the probability of survival to hatch in song sparrow nestmates. f and MLH were weakly, but significantly negatively correlated. Inbreeding coefficient predicted the probability of survival to hatch. In contrast, MLH did not predict the probability of survival to hatch nor did it account for residual variation in survival to hatch after statistically controlling for the effects of f. These results are consistent with the expectation that heterozygosity-f correlations will be weak when mean and variance in f are low. Our results also provide empirical support for recent simulation studies, which show that variation in MLH among siblings with equal f can be large and may obscure MLH-fitness relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".