Multi-sensor Analyses of Vegetation Indices in a Semi-arid Environment
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
Multi-sensor comparisons are sometimes used due to limited image availability and temporal coverage by a single sensor. However, multi-sensor comparability is not well documented. Factors affecting direct comparability such as atmospheric conditions, landscape heterogeneity, landscape changes, and sensor characteristics are difficult to quantify. This study compared several vegetation indices (VIs) from multi-sensor data to determine if VIs are comparable across scales and sensors. Within-sensor comparisons demonstrate that VIs are consistent across spatial resolutions indicating a direct multi-scale comparability. However, among-sensor comparisons indicate that VIs calculated from different sensors are not comparable with one another regardless of spatial resolution. Sensor-specific characteristics appear to offer the best explanation for the observed results.
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 | 0.001 | 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 | 0.001 |
| 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 it