Vulnerability Study by DRASTIC Method for Eocene and Turonian Aquifers in the Tadla Plain
Bibliographic record
Abstract
In numerous regions across Morocco, groundwater quality has undergone a significant decline in recent years, primarily due to factors such as agricultural expansion, improper solid waste disposal, and industrialization. This study presents an analysis of the vulnerability of two primary shallow aquifers, namely the Turonian and Eocene. The vulnerability assessment entails the evaluation of the aquifers' susceptibility to various forms of surface pollution, based on the physical characteristics of their surroundings. Within the Moroccan context, the DRASTIC method, integrated with Geographic Information Systems (GIS), emerges as the most suitable approach. This method involves the processing and analysis of seven factors pertaining to the three soil compartments, encompassing land cover, the unsaturated zone, and the saturated zone. Each chosen parameter is assigned a weight, reflecting its significance concerning groundwater protection. Notably, the water table's depth and the nature of the vadose zone exert the most substantial influence, followed by factors like recharge. Conversely, topography exhibits minimal impact, with soil type following suit. Lastly, the nature of the aquifer medium and its conductivity carry a moderate degree of influence. Each parameter is further categorized into classes, each defined by a specific rating. To derive an all-encompassing measure of vulnerability, the weights and scores of these various factors are synthesized through an additive model, yielding the comprehensive "DRASTIC Index." Subsequently, this index is employed to assess the intrinsic vulnerability level of the Turonian and Eocene aquifers by superimposing the seven index maps.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".