Bibliographic record
Abstract
For some discreet traits, breeders may need to break genetic linkage between a desirable trait and an undesirable trait. Breeders need to be able to determine the minimum number of plants to grow to have a specified probability of identifying at least one plant that is recombinant between two linked loci. Since 1931, recurrence equations have been available to determine the genotypic frequencies of each genotype when two loci are linked as the level of inbreeding changes. However, these genotypic frequencies have not been published in a tabular form that would be helpful to the applied plant breeder. The objectives are to: (1) provide genotypic proportions as the intensity of linkage and the level of inbreeding increases; (2) determine the most efficient method of identifying a homozygous recombinant genotype as the level of inbreeding and linkage intensity are varied. Numerical examples and formula are provided to determine the number of plants that must be grown to have a given probability of success of identifying a specified number of recombinant types. For close linkage, the number of plants that must be genotyped is greatly decreased by waiting until the population is highly inbred. Key words: Genetic recombination, inbreeding
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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".