Analyse de l'incertitude de quatre modèles de phytoprotection relative à l'erreur des mesures des variables agrométéorologiques d'entrée
Bibliographic record
Abstract
Outputs uncertainty analysis of four crop protection models relative to agrometeorological inputs measurement errors. The use of computer models in crop protection increases our management and forecast capacities as well as it allows to reduce the increasing pressure of agricultural activity on natural resources by the optimization of phytosanitary products use. The CIPRA system (Centre Informatique de Prévision de Ravageurs en Agriculture), a modeling tool gathering, under a common computer frame, several forecasting models using standard weather data (temperature, wind, precipitation, relative humidity), is one of the first Canadian operational decision support systems in crop protection. Since models are just a simplification more or less representative of the corresponding biophysics system, it is of primary importance to study the implications and the limitations of their application by determining the uncertainty level on model outputs. The purpose of the present paper was to evaluate the impact of uncertainty associated with weather inputs measurement on the outputs of four models within CIPRA system. These models are the Cercospora blight of carrots (Cercospora carotae (Pass.) Solheim), the onion leaf blight (Botrytis squamosa J.C. Walker), the carrot weevil (Listronotus oregonensis (LeConte)) and the European corn borer (Ostrinia nubilalis (Hübner)). This objective was achieved using the relative sensitivity and the uncertainty propagation. Results show that the cercospora blight model is more sensitive to temperature than with relative humidity. In this case uncertainty on the relative humidity is a significant source of error in the outputs. The onion leaf blight model is primarily sensitive to the relative humidity fluctuations. The relative humidity is, therefore, the principal source of error in the model outputs. Finally, the two insects models (weevil and borer) are more sensitive to the maximum temperature than to the minimum temperature. Uncertainties on the two models outputs are low. In addition to the evaluation of the reliability and level of significance of the results provided by the four models, this study allows to identify the most significant meteorological variables for the models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".