Effect of 3<scp>B</scp>, 5<scp>A</scp> and 3<scp>A QTL</scp> for <scp>F</scp>usarium head blight resistance on agronomic and quality performance of <scp>C</scp>anadian winter wheat
Bibliographic record
Abstract
Abstract The objectives of this study were to investigate (i) the correlations between Fusarium head blight (FHB) index, deoxynivalenol (DON) accumulation and percentage of Fusarium‐damaged kernels (FDK) with agronomic and quality traits and (ii) the effect associated with the presence of single QTLs for FHB resistance on agronomic and quality traits in winter wheat. The population was derived from the cross between ‘RCATL33' (FHB resistance derived from ‘Sumai 3’ and ‘Frontana’) and ‘RC Strategy’. Parental lines and recombinant inbred lines (RILs) were genotyped with SSR markers associated with the 3B, 5A and 3A QTLs. The population was planted in FHB‐inoculated nurseries and in agronomy trials. Lines in the 3B QTL class had the lowest FHB index, DON content and FDK level and did not have a significantly lower yield, thousand kernel weight or protein content compared with the lines grouped in other QTL classes (including no QTL class). Marker‐assisted selection of the 3B QTL for FHB resistance into high‐yielding FHB‐susceptible winter wheat is the recommended approach for the development of lines with increased FHB resistance without significant yield and quality penalties.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".