An integrated technique for delineating groundwater contaminated zones using geophysical and remote sensing techniques: a case study of Al-Quway’iyah, central Saudi Arabia
Bibliographic record
Abstract
Geophysical and remote sensing techniques were carried out to raise the groundwater quality and delineate expected contaminated zones near an open-waste disposal site in Al-Quway’iyah, central Saudi Arabia. An extracted digital elevation model (DEM) from very high resolution (VHR) satellite images was used to define the surface lineaments and prevailing flow path directions present in the study area. Remote sensing results indicated that groundwater in the Al-Quway’iyah metropolitan area flows through a complex network of interconnected fractures, which are controlled by the regional geological and structural settings of the area. Seismic refraction profiling was applied to delineate the depth to the groundwater table and bedrocks, and to locate those faults that may provide pathways to contaminants associated with the open-waste disposal site in the survey area. The results showed that possible subsurface groundwater contamination zones are mainly associated with weaker–fractured zones underlying the surface lineaments. This survey suggests that adequate integration of remote sensing and seismic refraction data can be applied to map spatial distribution of contaminants efficiently. It can facilitate future studies to be conducted for environment and human health hazard appraisal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".