Spectral and thermal mapping of desert surface sediments for agricultural development
Bibliographic record
Abstract
A combination of multispectral, thermal and microwave data obtained from space and supported by ground measurements are used to investigate the surface sediment characteristics of a desert plain area in Egypt (El-Gallaba Plain, NW of Aswan).This plain once hosted an ancestral river system that is nowadays largely covered by aeolian and gravelly sands, and thus, only detectible with radar and thermal images.The methodology consists of extracting thermo-physical and textural parameters to guide and improve supervised spectral classification results.The results show that surface mineralogy (obtained from spectral information) correlates strongly with surface emissivity, whereas grain size and surface roughness strongly correlates with apparent thermal inertia.Furthermore, several broad strips of thermal cooling-anomalies are arranged in a linear fashion and diagonally crossing the alluvial basin.The sediments within these strips show very different textural, thermo-physical and compositional characteristics with respect to the surrounding areas suggesting that they were deposited under different depositional environments such as structurally controlled linear basins.These tectonic depressions were confirmed by ground penetrating radar and could be promising areas for groundwater accumulation and exploration enabling agricultural development in the El-Gallaba Plain of the Western Desert in Egypt.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 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".