Test-weight and weathering of spring wheat
Bibliographic record
Abstract
Wet weather often delays harvest and results in a grade reduction of wheat because of a decrease in test-weight, an important grading factor in Canada. The objectives of this study were to assess the effect of delayed harvest on test-weight loss of 14 Canadian wheat cultivars representing three different classes, and to develop a screening strategy for retention of test-weight for breeding programs. Non-weathered test-weight (NWTWt), weathered test-weight (WTWt), and test-weight loss (TWtLoss; i.e. NWTWt – WTWt) in the field, averaged over five field locations and 2 yr were similar within the CPS, CWAD and CWRS wheat classes, although there were genotypic differences for all three variables. Because test-weight requirements for the top grades are higher in the CWAD class than in other classes, durum cultivars would be more susceptible to downgrading during wet harvests. Historical data from the Durum Wheat Co-operative Test also suggests that, since 1950, the mean test-weight of the genetic lines has decreased by 3.7 kg hL−1, and is now close to the minimum for grade #1 in the CWAD class. Most of the decrease in test-weight observed over several weeks in the field could be simulated by a single 5 – 10 min soaking of non-weathered seed in the laboratory. Linear regression analyses of both field and laboratory samples indicated that 90% of the genotypic variation in weathered test-weight could be attributed to differences in the NWTWt. These results suggest that the screening strategy for retaining test-weight should focus on selection for increased NWTWt. This is much simpler than screening for high WTWt or low TWtLoss, which requires soaking of the seed or field weathering. Key words: Triticum aestivum, Triticum turgidum, quality
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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 | 0.000 | 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) | 0.001 | 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".