Bibliographic record
Abstract
The fitness consequences of inbreeding and outbreeding have intrigued biologists for a long time. Recently a measure of relatedness of parental haplotypes has been proposed called mean d(2). This measure is based on a stepwise mutational process and therefore is tailored to microsatellite genetic markers. Theoretical work suggests that mean d(2) typically is less suited for measuring fitness consequences due to close inbreeding rather than heterozygosity. However, mean d(2) may be more appropriate than heterozygosity for measuring divergence times over longer time scales and thus for detecting outbreeding depression. Here, simulations are used to (1). identify appropriate standardization coefficients and transformations for mean d(2), and (2). evaluate mean d(2) as a measure of divergence time of parental lineages over time scales up to 10000 generations. Results show that mean d(2) is a linear predictor of divergence time. The coefficient of variation of mean d(2) approaches a constant value with increasing divergence time and therefore logarithm transformation is appropriate to restore homoscedasticity. When mutation rates and sizes are known for each locus they can be incorporated into a standardization coefficient to increase the precision of mean d(2). As few as 10 loci can explain more than 70% of the variation in divergence time between lineages. While heterozygosity outperforms mean d(2) at detecting differences in divergence time over relative short time periods (<or=1000 generations), mean d(2) can outperform heterozygosity at detecting differences over longer time periods (>or=1000 generations). However, gene flow of as little as 1% per generation can significantly reduce the ability of either mean d(2) or heterozygosity to estimate divergence time.
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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".