Mapping spatial distribution of a disease forecasting model using precipitation and relative humidity measurements provided by weather radar
Bibliographic record
Abstract
Many applications of remote sensing in agriculture have been developed since 60 years but mostly since the launch of Landsat 1 in 1972. With time, improvements in spatial, spectral and temporal resolution have been made and generated a resurgence of remote sensing popularity. Combined with agricultural systems modelling, remote sensing data such as weather radar measurements can help to obtain an accurate tool in real-time for agricultural decision-makers. Indeed, precipitation and relative humidity (RH) could become available for the agricultural decision-makers using the McGill Doppler S-band radar. At present, precipitation measurements are available with a spatial resolution of 1 km up to a range of 240 km and RH data could be available with a resolution of a few kilometres up to a range of 40 km. Both weather variables could be available with a time scale of 5 min if requested. These measurements would compensate for the actual lack of a dense weather station network prevailing in southern Quebec. So far, the reliability of weather radar measurements has been tested by the scientific community for precipitation data but has never been tested for the RH data. In this study, a comparison between RH measured at three weather stations and RH calculated from weather radar measurements was made using consecutive time interval of 240 hours in 1997 and 336 hours in 1998. A valid t-test designed for simple linear regression analysis with two time series as dependent and explanatory variable, and based on the first-difference ratios (FDR) of the time series clearly showed that RH calculated from radar measurements is comparable to the one measured at weather stations. Thereafter, the possibility of integrating weather radar measurements (precipitation and RH) in a geographic information system (GIS) to map the variability of a crop disease was verified. Results indicated the potential of weather radar measurements in agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".