Genotype difference in kernel discolorations in CWRS and CWAD wheats
Bibliographic record
Abstract
Kernel discolorations of wheat, such as black point (including smudge and penetrated smudge), red smudge and Fusarium-damaged kernel (FDK), are important downgrading factors in western Canada. This study was undertaken to determine cultivar differences in incidences of these kernel discolorations and their downgrading effects in CWRS and CWAD wheats. Grain samples were taken at maturity from six CWRS and four CWAD cultivars in a four-year (1997-2000) field study at six sites: one in the Grey soil zone, two in the Brown soil zone and three in the Black soil zone in western Canada. For CWRS cultivars, Garnet tended to have lower incidences in black point, smudge and FDK and resulted in the least downgrading compared with other CWRS cultivars. For CWAD cultivars, Kyle tended to have lower incidence of black point, Plenty had less smudge caused downgrading effects, DT 369 had lower penetrated smudge incidence, but higher red smudge incidence, and Plenty tended to have severer FDK-caused downgrading effects compared with other CWAD cultivars. CWAD class had higher frequency of downgrading than CWRS class and it usually had higher incidences of all kernel discolorations (black point, smudge, penetrated smudge, red smudge and FDK) and greater downgrading effects than CWRS class.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".