Evaluation of disease forecasting variables for sclerotinia stem rot (<i>Sclerotinia sclerotiorum</i>) of canola
Bibliographic record
Abstract
Selected environmental, crop and pathogen variables were sampled weekly from winter and spring canola crops before and during flowering and evaluated for the ability to predict sclerotinia stem rot, caused by Sclertinia sclerotirum. Linear and nonlinear relationships were examined among variables but, because no strong correlations were observed between final disease incidence and any of the variables tested, a categorical approach (e.g., disease severity) was used instead. Disease severity in individual crops was categorized as low (< 20% diseased plants) or high (> 20% disease), and differences in weekly rainfall, soil moisture, crop height, percentage of petal infestation, and number of apothecia m −2 and clumps of apothecia m −2 were significantly associated with differences in disease severity within or between years. Two disease prediction models were compared for the ability to predict low or high disease severities using petal infestation alone, or petal infestation in combination with soil moisture. The model that included petal infestation and soil moisture predicted more fields correctly than the model using petal infestation alone, but the accuracy of both was affected by the timing of soil moisture measurements in relation to petal infestation, and threshold values used in discriminating categories of soil moisture and petal infestation. Key words: Brassica rapa, Brassica napus, Sclerotinia sclerotiorum, disease prediction
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".