Estimating Surface Wetness on Plants
Bibliographic record
Abstract
Plant surface (leaf, stem, petiole, etc) wetness or leaf wetness is an important agricultural weather variable for the prediction of plant diseases (Yarwood, 1978). The estimation of surface wetness has received considerable attention from a diverse group of scientists but unfortunately, no standard for measuring surface wetness has been widely accepted. Since surface wetness is not a true meteorological variable, as is temperature or precipitation, its measurement has not been routinely made as part of a general observation program by most national meteorological agencies. The main problem of not observing surface wetness on a routine basis has been the lack of agreement about a standard sensor design and protocol for the use of sensors. In addition, most sensors measure surface wetness indirectly and have different physical properties. In order for a sensor to represent a particular crop, plant organ, or environment, the sensor should be calibrated with visual observations of surface wetness. Unfortunately, the collection of visual observations is labor intensive and difficult, since there is no universally accepted definition for plant surface wetness. In this chapter, we will attempt to define surface wetness in physical terms and review methods for determining surface wetness. We also will consider solutions to the lack of surface wetness standardization from both a measurement and simulation perspective. And finally, we will also recommend the most suitable protocols for estimating surface wetness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".