The genetic architecture of flowering time and related traits in two early flowering maize lines 2007 [Canada]: Bioinformatics and quantitative genetics
Bibliographic record
Abstract
Flowering time is the major factor in determining maize (Zea mays L.) maturities. Genetic bases of flowering time and other agronomically important traits were examined in a set of interheterotic-pattern recombinant inbred lines (RILs). The RILs were developed from crossing the short-season Iodent inbred line CG60 with the short-season Stiff Stalk inbred line CG102. Recombinant inbred lines were derived through single-seed descent (S-RILs) or intermated for three generations before inbreeding (I-RILs), thereby increasing recombination. In this study the genetic means, covariances, and variances of flowering time (days to anthesis, DA, and days to silking, DS) and a number of flowering-time-associated traits, leaf number (LN), plant height (PH), and stay green (SG), in a set of short-season, interheterotic-pattern recombinant inbred lines (RILs) representing two levels of intermating were examined. The objectives were: (1) to evaluate the genetic basis of DA, DS, LN, PH, SG, and canopy reflectance traits within the short-season parental lines, (2) to evaluate genetic and phenotypic correlations between DA, DS, LN, PH, SG, and canopy refl ectance traits, and (3) to evaluate whether disruption of putative coupling and repulsion phase linkage blocks causes changes in population means, genetic variances, and genetic correlations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".