Marker‐inferred relatedness as a tool for detecting heritability in nature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a perspective of how inferred relatedness, based on genetic marker data such as microsatellites or amplified fragment length polymorphisms (AFLPs), can be used to demonstrate quantitative genetic variation in natural populations. Variation at two levels is considered: among pairs of individuals within populations, and among pairs of subpopulations within a population. In the former, inferred pairwise relatedness, combined with trait measures, allow estimates of heritability 'in the wild'. In the latter, estimates of QST are obtained, in the absence of known heritabilities, via estimates of pairwise FST. Estimators of relatedness based on the 'Kronecker operator' are given. Both methods require actual variation of relationship, a rarely studied aspect of population structure, and not necessarily present. Some conditions for appropriate population structures in the wild are identified, in part through a review of recent studies.
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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) | 0.001 | 0.001 |
| 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 | 0.003 | 0.001 |
| 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 it