Reducing flea-beetle feeding wounds on canola seedlings with foliar insecticide failed to improve blackleg control
Bibliographic record
Abstract
In earlier studies, it was found that Leptosphaeria maculans could readily infect canola via fresh wounds but would require extended leaf wetness to infect intact tissues. We hypothesized that flea beetle feeding wounds might facilitate L. maculans infection under field conditions. Multi-year plot trials were conducted in western Canada to assess the effect of reducing flea beetle feeding wounds on canola seedlings with in-crop insecticide on subsequent blackleg infection. The experiment encompassed susceptible and resistant cultivars seeded into canola stubble from the previous year, with pyrethroid insecticide applied weekly commencing at trace feeding (<5%) to mitigate the insect damage. Fluopyram seed treatment was used as an additional treatment against early blackleg infection. Results from eight station years showed moderate feeding damage by flea beetles and generally high blackleg pressure, with the average disease incidence > 80%. Despite reduced feeding damage due to in-crop insecticide treatment, significant blackleg reduction was not observed. A greenhouse study revealed no correlation between puncture wounds on cotyledons and successful stem infection under high-inoculum conditions, suggesting that reduced insect feeding might not effectively counteract the heavy inoculum pressure resulting from canola residue in field trials. Fluopyram seed treatment did not reduce blackleg substantially, while resistant cultivars consistently exhibited effectiveness. Furthermore, harvested seed from plots with lighter blackleg often displayed lower levels of L. maculans inoculum contamination.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".