Water stress detection as an indicator of red palm weevil attack using worldview-3 data
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 R2values (≈ 95%) using second order polynomial function (p2of 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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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) | 0.000 | 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".