A note on the bias of genetic distances in linkage maps based on small samples for backcrosses and intercrosses with complete dominance
Bibliographic record
Abstract
This paper investigates the bias (the difference between the expectation (mean) of an estimator and its true value) of genetic distances for small samples. Exact results on this bias have not received much attention in genetic mapping literature. We show that bias drops quickly with increasing sample size for both a backcross population and an F2 in coupling. By contrast, bias may be substantial even for larger sample size for an F2 when markers are in repulsion. It is concluded that Karlin's map function should be used with care when mapping is done using an F2 population. The same note of caution applies to other map functions such as Haldane's and Kosambi's. Finite-sample bias of these latter functions cannot be assessed because of the nonexistence of an expected value, but their median bias is similar to that of Karlin's function.
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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 | 0.059 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".