Utilization of Space-Borne Imagery for Lithologic Mapping: A Case Study From Um Had Area, Central Eastern Desert, Egypt
Bibliographic record
Abstract
Remote sensing data are used to discriminate between the different lithologies covering the Um Had area, Central Eastern Desert of Egypt. Image processing techniques applied to the Enhanced Thematic Mapper (ETM+) data are used for mapping and discriminating the different basement lithologies of Um Had area. Principal component analysis (PCA), minimum noise fraction (MNF) transform and band rationing techniques provide efficient data for lithological mapping. The study area is underlain by gneisses, ophiolitic melange assemblage (talc-serpentinite, metagabbro, metabasalt), granitic rocks, Dokhan volcanics, Hammamat sediments and felsites. The resulting gray-scale PC2, PC3 and PC4 images are best to clearly discriminate the Hammamat sediments, amphibolites and talc-serpentinites, respectively. The gray-scale MNF3 and MNF4 images easily discriminate the felsites and talc-serpentinites, respectively. The band ratio 5/7 and 4/5 images are able to delineate the talc-serpentinites and Hammamat sediments, respectively. Information collected from gray-scale and false color composite images led to generation of detailed lithologic map of Um Had area.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".