Response of lentil cultivars to Sclerotinia white mold and Botrytis grey mold infection under irrigated and dryland conditions
Bibliographic record
Abstract
Diseases such as Sclerotinia white mold (SWM), caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary, and Botrytis grey mold (BGM), caused by the fungus Botrytis cinerea Pers., may be limiting factors for lentil production in wetter areas of Alberta, Canada. Field trials were conducted at the Lethbridge Research and Development Centre from 2013 to 2015 to evaluate the response of lentil cultivars to SWM and BGM and yield impacts. Ten lentil cultivars from five market classes were evaluated under irrigated and dryland plots with two planting densities (120 and 160 plants·m−2). Year and irrigation had the largest effect on disease incidence, with highest SWM incidence occurring under irrigation in 2013, followed by 2014 and 2015. Conversely, BGM incidence under irrigation was highest in 2015 and lowest in 2013, but levels were lower than SWM. Significantly (P < 0.05) lower disease incidences were observed in dryland plots, which also produced higher yield than irrigated plots. Cultivars varied significantly in SWM incidence and yield under irrigated and dryland conditions, perhaps due to variable disease pressure, but there was no consistent trend in cultivar performance. BGM incidence was similar in cultivars, but differed among years. These findings indicate that SWM may be a limiting factor to lentil production in wetter areas, as the 10 cultivars from five market classes tested were all highly susceptible to SWM.
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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".