Modeling Water Stress as an Indicator of Red Palm Weevil Infestation Using Field Sampling, Worldview-3 Reflectance, and Laboratory Analysis
Bibliographic record
Abstract
This study focuses for the first time on the water content modeling as an indicator for red palm weevil (RPW) stress-attacks using field sampling, Worldview-3 (WV-3) reflectance measurements, water content estimation at the laboratory (WC-Lab), several water stress indices (WSI) and statistical analysis. Based on field identification and sampling, 100 date palm trees were considered and divided into four classes of RPW stress-attacks: dead, severely attacked, moderately attacked, and healthy trees (young and mature trees). Spectral measurements were acquired over each sample using Analytical Spectral Devices (ASD). Then, they were resampled and convolved using WV-3 spectral response profiles and the Canadian radiative transfer code (CAM5S). For model calibration, only 80 samples were considered, while 20 samples were used for validation purposes. Obtained results indicate that the proposed models based on PTWSI-4 and SRWI offer an important alternative to discriminate among different levels of RPW stress-attack. They discriminate significantly among the considered stress classes (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≥ 0.85) using second order regression . The validation of PTWSI-4 model revealed a significant correlation with the WC- <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lab</sub> values (R2 of 0.95), and an acceptable RSME of 8.5 %.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".