Developing standardized methods for breeding preharvest sprouting resistant wheat, challenges and successes in Canadian wheat
Bibliographic record
Abstract
Preharvest sprouting (PHS) in spring wheat ( Triticum aestivum L.) and durum wheat ( T. turgidum L. var durum ) causes significant economic losses due to a reduction in grain yield, grain functionality and viability of seed for planting. Average annual estimated losses in Canada are about $100 million. Genetic resistance to PHS reduces these losses. Development of PHS resistant cultivars is complicated by the effects of factors under genetic control, such as spike morphology, seed dormancy, environment, and kernel diseases. Resistance to PHS has been a breeding priority since the late 1960s. Development of RL4137, which is the primary source of PHS resistance in the Canada Western Red Spring market class, has led to cultivar improvements. A white-seeded derivative of RL4137 is the primary source of PHS in the Canada Prairie Spring White and Canada Western Hard White Spring wheat market classes. Procedures to select for PHS resistance vary among breeding programs, market classes and by degree of inbreeding. Methods include artificial sprouting of intact spikes, germination tests, natural weathering in field trials, artificial weathering trials, and indirect assessment of sprouting by measuring Hagberg falling number. Although many genetic loci have been attributed to preharvest sprouting resistance, application of molecular markers is currently limited due to the complex inheritance of the trait. In Canada, cultivars are characterized for their relative level of PHS resistance and the information is made available to producers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".