Effect of <i>Lygus</i> spp. and <i>Botrytis</i> spp. on faba bean (<i>Vicia faba</i> L.) seed quality — are there insect–pathogen interactions?
Bibliographic record
Abstract
Lygus bugs and Botrytis fungal pathogen, the causal agent of chocolate spot in faba bean, can cause necrotic spots on faba bean seeds, thereby reducing market value. The mid-pod stage is the most susceptible stage for chocolate spot development and Lygus infestation in faba beans. Therefore, we hypothesised that the concomitant presence of Lygus spp. and Botrytis spp. might increase seed necrosis. Hence, the study was conducted to determine (i) the spatial and local distribution of chocolate spot and Lygus spp. in central and southern Alberta, and (ii) the association of chocolate spot disease severity and Lygus abundance. Chocolate spot and Lygus were present in all the counties surveyed. Chocolate spot had a negative association with Lygus abundance, but only the latter was significantly associated with seed necrosis. Botrytis spp. were frequently isolated from seeds despite the lack of expression of chocolate spot on the foliage. No significant effect of Lygus abundance on Botrytis isolation from seeds was found. Therefore, seed quality losses can occur both due to the fungal pathogen and the insect, which likely occupy different niches influenced by microclimate. Economic thresholds and management strategies will be required to keep insect populations and disease progression under check.
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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 | 0.001 | 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".