A new model to estimate leaf wetness duration in an apple orchard
Bibliographic record
Abstract
Fungal disease infection risk is strongly dependent on the climatic conditions and on the plant microclimate. Leaf Wetness Duration (LWD), i.e. the time that free water remains on leaves in relation to rain or dew events is for many fungal species the significant parameter linking the epidemic risk to environment humidity. However modeling LWD has always been a complicated task, mainly because of the specific variables needed such as solar radiation and light and water distribution in the canopy, requiring local measurements. Our recent works led to the development of a framework improving the physical modeling of leaf wetness duration without on-site measurements – thanks to high resolution weather nowcasts and forecasts – and a minimum of empirical values, in order to make the model portable to different locations and pathosystems. The model takes into account the tree architecture and leaves physical and phonological properties.The model has been validated for apple McIntosh cultivar using past local weather variables measured in 2014 St-Bruno-de-Montarville, Quebec, Canada. Hourly simulation during the whole year produced a leaf wetness duration RMSE of 26 minutes.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".