Utilizing potential field data to support delineation of groundwater aquifers in the southern Red Sea coast, Saudi Arabia
Bibliographic record
Abstract
In this paper potential field data are interpreted to map the undulation of the basement surface, which represents the bottom of the water bearing zones, and to delineate the tectonic framework that controls the groundwater flow and accumulation in the southern Red Sea coastal area of Saudi Arabia. The interpretation reveals that the dominant structural trend is a NW (Red Sea) trend that resulted in a series of faulted tilted blocks. These tilted blocks are dissected by another cross-cut NE trend which shapes and forms a series of fault-bounded small basins. These basins and the bounded structural trends control and shape the flow direction of the groundwater in the study area, i.e. they act as groundwater conduits. Furthermore, the present results indicate that volcanic intrusions are present as subsurface flows, which hinder the groundwater exploration and drilling activities in most of the area; in some localities these volcanic flows crop out at the surface and cover the groundwater bearing formations. Furthermore, the gravity and magnetic data interpretation indicates the possible existence of a large structural basin occupying the southeastern side of the study area. This basin is bounded with NW and NE trending faults and is expected to be a good host for groundwater aquifers; thus it is a promising site for hydrogeological investigation.
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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 | 0.001 |
| 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 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".