Bibliographic record
Abstract
Q(ST) is a commonly used metric of the degree of genetic differentiation among populations displayed by quantitative traits. Typically, Q(ST) is compared to F(ST) measured on putatively neutral loci; if Q(ST)=F(ST), this is taken as evidence of spatially heterogeneous and diversifying selection. This paper reviews the uses, assumptions and statistics of Q(ST) and F(ST) comparisons. Unfortunately, Q(ST)/F(ST) comparisons are statistically challenging. For a single trait, Q(ST) must be compared not to the mean F(ST) but to the distribution of F(ST) values. The sources of biases and sampling error for Q(ST) are reviewed, and a new method for comparing Q(ST) and F(ST) is suggested. Simulation results suggest that the distribution of neutral F(ST) and Q(ST) values are little affected by various deviations from the island model. Consequently, the distributions of Q(ST) and F(ST) are well approximated by the Lewontin-Krakauer prediction, even with realistic deviations from the island-model assumptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".