Breeding indicators for high-yielding field pea under normal and heat stress environments
Bibliographic record
Abstract
The warming Canadian summers have become a major abiotic stress to crops, including pea. In the past decade, attempts were made in the understanding of heat stress effect and genomic mapping for heat-responsive traits in field pea. In this study, a new recombinant inbred line population (PR-24) consisting of 39 lines was tested in 6 trials in the summers of 2020 (near normal weather conditions) and 2021 (hot/dry conditions). PR-24 was phenotyped for days to flowering (DTF), days to maturity, plant height, lodging, yield components, plot yield, and seed quality traits. Plant height could be an effective indicator for yield prediction, because its correlation with plot yield was significantly positive in all six trials despite varying degrees of heat and drought stress. Under normal summer weather conditions in 2020, relatively late maturity was correlated with greater seed yield; under heat/drought stress conditions in 2021, successful pod development on the main stem was important for final plot yield. Linkage mapping was used to dissect the genomic regions associated with the measured traits. Four QTLs were identified over multiple trials, one each for DTF (chromosome 7), reproductive node number (chromosome 5), pod number (chromosome 2), and seed protein concentration (chromosome 5). Furthermore, two indices, i.e., stress tolerance index and geometric mean yield, previously used in drought tolerance assessment were validated as useful criteria for heat tolerance assessment in this study.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".