THE IDENTIFICATION OF THE SELF-INCOMPATIBILITY ALLELES IN A GRASS POPULATION
Bibliographic record
Abstract
ETHODS to estimate the sizes of the two series of inconipatibility M alleles which operate in self-incompatible grasses, were given in two previous papers (LUNDQVIST, 1962; 1964).In a population at genetic equilibrium the numbers of alleles at S and Z can be deduced from the frequencies in which selected specific SZ gene prrirs or individuril incompmtibility genes occur.The first method, though rapid and simple, is the less efficient one since the esiimate obtained for one series depends on the number of alleles postulated for the second series.The second method, under ideal conditions of genetic equilibrium, is capable of giving reliable estimates of the size of an allelic series, although it is more cunibersonie and sensitive to sampling biases.The niost efficient method would be a strict identification of the indioidurtl members within the two nllelic series.Data of this kind have the advantage that they require no far-reaching assumptions regarding the genetic equilibrium of the population.The present paper reports methods and data from such an investigation in Meadow Fescue, Fc.stiicn prntensis HUDS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".