Biophysiological spectral indices retrieval and statistical analysis for red palm weevil stressattack prediction using Worldview-3 data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The red palm weevil (RPW) is a dangerous invasive insect species that is causing severe damage to date palm trees around the world. This study focuses for the first time on the detection and discrimination among different stages of RPW stress-attacks using bio-physiological spectral indices and statistical analysis of satellite image data. A total of 27 different indices were assessed using new technology Worldview-3 image data. Preprocessing included correction for atmospheric effects, sensor radiometric calibration drift, and imaging geometry. Based on field identification and localization using GPS, four palm tree classes were considered: healthy; attacked-treated; attacked-untreated; and severely attacked (a shadow class was also included). Twelve vegetation indices (VIs) and fifteen chlorophyll indices (CIs) were evaluated using visual (maps) and statistical analysis, with validation against field observations (175 sample points, 35 per class). The results showed that the Structure Insensitive Pigment Index (SIPI) and the Green Normalized Difference Vegetation Index (gNDVI) were the most sensitive to palm tree bio-physiological agitation caused by RPW attacks. They discriminated significantly 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 obtained, respectively, as 93% and 98% for SIPI and gNDVI. According to these first results, it was concluded that remote sensing science can be a promising alternative for RPW detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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