Predicting field diseases caused by <i>Sclerotinia sclerotiorum</i> : A review
Bibliographic record
Abstract
Abstract Predicting diseases caused by Sclerotinia sclerotiorum in field crops remains difficult, and published literature is largely inconsistent in finding significant relationships with environmental and agronomic factors for various life stages of the fungus. A scoping review was performed to synthesize the current quantitative insights into the role of the environment on the life cycle of S . sclerotiorum and the relationships between various life stages of the fungus and final disease expression under field conditions. For most variables, relationships with stages of the life cycle showed a wide range of responses ranging from closely related (high correlations or r 2 values) to not related at all. The effects were often moderated by year, location and/or the presence of another variable such as irrigation, soil type, row spacing or cultivar. Studies that based analysis on a more nuanced understanding of pathogen biology rather than looking only at linear relationships tended to find stronger associations between variables. Yield was consistently negatively associated with disease levels, but cultivar, year, location and planting density were all important determinants of yield. Suggestions for improvement to future research in predictive model development of S . sclerotiorum diseases are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".