Quantitative resistance against an isolate of <i>Leptosphaeria maculans</i> (blackleg) in selected Canadian canola cultivars remains effective under increased temperatures
Bibliographic record
Abstract
Blackleg disease, caused by Leptosphaeria maculans , is a serious threat to canola production in western Canada. While specific major resistance ( R ) genes can be effective, they can also be eroded rapidly by a shift in pathogen race composition. Quantitative resistance ( QR ) has the potential to provide more durable, if less complete, protection. However, the effectiveness of QR may vary widely in the field. It has long been suspected that elevated temperatures may limit the expression of QR . To test this hypothesis, the infection development of blackleg was assessed on three common Canadian canola cultivars (74‐44 BL , PV 530 G and 45H29) showing QR , with and without a heat treatment of 7 h daily exposure to 32 °C for 1 week during rosette to early flowering under controlled environment conditions. The impact of elevated temperature on the susceptibility to blackleg was compared with that of a moderate temperature with a 22 °C daytime high. A susceptible cultivar, Westar, was used as a control. When data from both temperatures were pooled, all three QR cultivars showed lower blackleg severity relative to Westar. Elevated temperatures increased blackleg severity in Westar only (in terms of the stem‐lesion length from the inoculation of the first true‐leaf petiole in trials involving Westar and 74‐44 BL) and in pooled data for disease severity index in trials involving Westar, PV 530 G and 45H29. These findings suggest that the QR traits in 74‐44 BL, PV 530 G and 45H29 are useful for blackleg management in western Canada, especially under warmer growing conditions when plants are at the rosette to early flowering stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".