Two weather‐based models for predicting the onset of seasonal release of ascospores of <i>Leptosphaeria maculans</i> or <i>L. biglobosa</i>
Bibliographic record
Abstract
Weather‐based models (Improved Blackleg Sporacle and SporacleEzy) to predict the date of onset of seasonal release from oilseed rape debris of ascospores of Leptosphaeria maculans or L. biglobosa , causes of phoma stem canker, were developed and tested with data from diverse environments in Australia, Canada, France, Poland and the UK. Parameters were estimated, using the same datasets from experiments in the UK and Poland, with an accuracy of root mean squared deviation ( RMSD ) of 7·4 (with a bias of −4·54, L . maculans ) and 8·5 (with a bias of 0·30, L. biglobosa ) days for Improved Blackleg Sporacle, and of 2·9 (with a bias of −0·06, L . maculans ) and 7·3 (with a bias of −1·18, L. biglobosa ) days for SporacleEzy. When tested with data independent of those used for parameter estimation, overall predictions agreed well with observed data in five countries, both for Improved Blackleg Sporacle ( R 2 = 0·96, slope = 1·00, standard error = 0·03, P > 0·05, n = 46) and SporacleEzy ( R 2 = 0·96, slope = 0·98, standard error = 0·03, P > 0·05, n = 46). However, SporacleEzy performed better in Australia, Canada, Poland and the UK ( RMSD = 10·6, 9·7, 5·4 and 3·4 days, respectively) than Improved Blackleg Sporacle ( RMSD = 11·7, 11·0, 5·6 and 6·5 days, respectively). In contrast, the prediction from Improved Blackleg Sporacle ( RMSD = 8·0 days) was better in France than that from SporacleEzy ( RMSD = 15·9 days). Sensitivity analysis showed that better parameter estimation could improve the quality of prediction of SporacleEzy ( RMSD = 7·6 days) under French conditions. These models are capable of estimating the first seasonal release of ascospores of organisms causing phoma stem canker on oilseed rape under many climates and thus could contribute to development of strategies for control of the disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".