Bibliographic record
Abstract
With the increased effort in mapping linked loci, it is important to understand how the precision of the estimate of linkage intensity is influenced by the level of inbreeding and the sample size. For discreet traits, such as molecular markers, the standard error of linkage intensity has been determined for an F2 and backcross population. However, the standard error of linkage intensity has not been determined for discreet traits in the case of the F3 population, double haploid or recombinant inbred lines. The objective is to provide information to aid plant scientists in planning mapping experiments where a given level of precision is desired when estimating the intensity of linkage between two loci for F2 and F3 populations as well as double haploid and fully inbred lines. The precision associated with the estimate of the intensity of linkage is shown graphically as the type of population, the sample size, the intensity of linkage and the linkage-phase is varied. For discreet traits, such as molecular markers, the F2 population and use of co-dominant markers are the best choices to maximize precision when estimating any degree of coupling and repulsion-phase linkage. Key words: Linkage, precision, inbreeding, sample size, markers, molecular
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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 | 0.033 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".