Influence of irrigation and plant canopy architecture on white mould disease of dry bean
Bibliographic record
Abstract
White mould caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary is a major constraint to irrigated dry bean production in southern Alberta. Irrigation, coupled with dry bean canopy architecture, may influence white mould by creating conducive environmental conditions. Field experiments were conducted from 2015 to 2017 at Lethbridge to determine the effect of three irrigation levels and five dry bean genotypes with different canopy architectures on white mould. Sensors and data loggers were established to monitor micro-climate data including soil moisture within the top 5 cm, leaf wetness, and soil temperature under the canopy. Canopy porosity, lodging, flower infection, and white mould disease severity were also measured. Higher moisture within the top 5 cm of the soil, lower soil temperature, elevated leaf wetness, and higher white mould incidence were observed in high irrigation plots compared with medium and low irrigation plots. Cultivars varied for leaf wetness, porosity, and lodging. Although a significant interaction between irrigation and cultivar was detected, irrigation levels did not affect disease severity significantly. Lower disease severity and incidence were recorded in AAC Burdett and Island. These cultivars have an upright growth habit, high canopy porosity, and lodging resistance, and therefore, exhibited partial field resistance (avoidance) to white mould. Mean yield across all cultivars was not affected by irrigation; however, the highest yield occurred in the medium irrigation plots. A reduced level of irrigation and development of cultivars with both avoidance and partial physiological resistance may reduce white mould severity and incidence in dry bean fields in Alberta.
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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".