Water stress detection as an indicator of red palm weevil attack using worldview-3 data
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Bibliographic record
Abstract
This study focuses for the first time on the water stress detection and discrimination among different stages of red palm weevil (RPW) stress-attacks using water stress indices (WSI) and linear and second order polynomial statistical analysis. Different WSI were assessed using new technology Worldview-3 (WV-3) simulated data. Based on field identification, five palm tree classes were considered: dead, severely attacked, attacked-untreated, attacked-treated; and healthy trees. Spectral measurements were acquired over each sample using Analytical Spectral Devices (ASD). They were resampled and convolved using WV-3 spectral response profiles and the Canadian radiative transfer code (CAM5S). Results showed that the indices NDWI, SRWI, SIWSI-1, SIWSI-2 and NDII are sensitive to palm trees water agitation caused by RPW attacks. They discriminated among the considered classes with excellent R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values (≈ 95%) using second order polynomial function (p < 0.05). Nevertheless, they express the water content dynamic range about only 10% to 55%. New Palm Tree Water Stress Index (PTWSI) were proposed using WV-3 SWIR bands. They differentiated among the considered classes with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 90%, and enhanced significantly water content dynamic range for a maximum about 90% or 100%. According to these first results, it was concluded that remote sensing science using WV-3 data is a promising alternative for RPW detection based on WSI.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it