Linkage mapping and whole-genome predictions in canola (Brassica napus) subjected to differing temperature treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canola (Brassica napus L.) is grown on >8 Mha in Canada and is sensitive to high temperatures; therefore, research on breeding methodologies to improve heat-stress tolerance is warranted. This study utilised a doubled-haploid population created from two parents (PB36 and PB56) that differed in their ability to set seed following growth at high temperatures. The experiment was designed to identify potential quantitative trait loci (QTLs) responsible for conferring tolerance to increased temperatures, and to utilise this population as a test case for evaluating the prospects of whole-genome prediction. The population was phenotyped in a split-plot, randomised complete block experimental design at three locations with two planting-date treatments. The first planting date was during the normal planting period (control), and the second planting was timed to experience increased average temperatures (1.7°C, 2.0°C and 1.2°C) and increased number of days with maximum temperatures above the critical temperature of 29.5°C (4, 12 and 3 days). The stress treatment reduced yield on average by 16.7%. There were 66 QTLs discovered across the nine traits collected. Given the quantitative nature of the traits collected, the ability to use whole-genome prediction was investigated. The prediction accuracies ranged from 0.14 (yield) to 0.66 (1000-seed weight). Prediction had higher accuracy within the stress treatment than within the control treatment for seven of the nine traits, demonstrating that phenotyping within a stress environment can provide valuable data for whole-genome predictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it