Predicting airborne ascospores of <i>Sclerotinia sclerotiorum</i> through machine learning and statistical methods
Bibliographic record
Abstract
Abstract A main biological constraint of dry bean ( Phaseolus vulgaris ) production in Canada is white mould, caused by the fungal pathogen Sclerotinia sclerotiorum . The primary infectious propagules of S . sclerotiorum are airborne ascospores and monitoring the air for inoculum levels could help predict the severity of white mould in bean fields. Daily air samples were collected in commercial dry bean fields in Alberta, Manitoba and Ontario and ascospores were quantified using quantitative PCR. Daily weather data was obtained from in‐field weather stations. The number of ascospores on a given day was modelled using 63 different environmental variables and several modelling methods, both regression and classification approaches, were implemented with machine learning (ML) (random forests, logistic regression and support vector machines) and statistical (generalized linear models) approaches. Across all years and provinces, ascospores were most highly correlated with ascospore release from the previous day ( r ranged from 0.15 to 0.6). This variable was also the only variable included in all models and had the greatest weight in all models. Models without this variable had much poorer performance than those with it. Correlations of ascospores with other environmental variables varied by province and sometimes by year. A comparison of ML and statistical models revealed that they both performed similarly, but that the statistical models were easier to interpret. However, the precise relationship between airborne ascospore levels and in‐field disease severity remains unclear, and spore sampling methods will require further development before they can be deployed as a disease management tool.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".