Effect of wounding and wound age on infection of canola cotyledons by <i>Leptosphaeria maculans</i>, interacting with leaf wetness
Bibliographic record
Abstract
Blackleg, caused by the fungus Leptosphaeria maculans, is an important disease of canola. Both ascospores and pycnidiospores of the fungus can infect intact leaves under conditions of extended leaf wetness, with infection then progressing further into the stem. In western Canada, spring is typically cool and dry and additional factors may be involved for successful infection. This study was designed to assess the effect of wounding and wound age on cotyledon infection, to evaluate the likelihood that flea beetle-feeding injuries could contribute to increased disease. Infection of canola cotyledons occurred readily via mechanical wounds in the absence of leaf wetness when seedlings were spray inoculated with pycnidiospores of L. maculans, whereas no infection occurred on intact cotyledons even under 6-h leaf wetness. Wound age also played a role in susceptibility; wounded tissues were less susceptible when plants were kept in a greenhouse for 8 h or longer before inoculation, with substantially reduced infection success relative to fresh wounds. This wound-age effect was similar on susceptible and resistant canola varieties. A high temperature (25°C) seemed to favour the healing of wounds, reducing susceptibility when compared with control (21/16°C) and low (10°C) temperatures. Leaf wetness post-wounding may hinder healing, and high post-inoculation humidity (80–90% relative humidity) increased infection via older wounds slightly, relative to lower (50–60%) relative humidity. These data provide a rationale for studying the effect of improved flea beetle control on early L. maculans infection of canola.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".