Contribution of kernel size to grain yield potential and sample uniformity of winter wheat
Bibliographic record
Abstract
Improvements in agronomic practices and cultivars have permitted the successful production of winter wheat on the Canadian prairies. In this study, the seed size of eleven winter wheat varieties grown under dry land and irrigation in each of two years was measured to determine if kernel size and position in the spikelet were important restrictions to cultivar grain yield potential and sample uniformity. Varietal differences in the weight of kernels in the A and B positions in the spikelet varied by more than 20 percent indicating that there is considerable genetic variation available in the wheat gene pool for this character. Kernel size of the C and D positions decreased to approximately 75 and 50 percent, respectively, of the average A and B positions in both dry land and irrigation environments. Artificially reducing floret numbers by 25 and 50 percent to increase assimilate supply to the remaining seeds did not influence seed size under irrigation. In contrast, kernel weight increased as the number of spikelets spike-1 decreased indicating that assimilate supply during grain filling and not restrictions imposed by kernel size determine grain yield of winter wheat grown on dry land in western Canada.
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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 | 0.001 |
| 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 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".