Vulnerability assessment of the Toluca Valley aquifer combining a parametric approach and advective transport
Bibliographic record
Abstract
Groundwater vulnerability assessment is an important task in water resources and land management. Depending on the availability of data and the complexity of the hydrogeological conditions, different approaches can be adopted. As an alternative, this study involves the use of a combined approach based on vulnerability methods and advective particle tracking to better understand the susceptibility to contamination in the Toluca valley aquifer. An intrinsic vulnerability map (DRASTIC) was used to identify areas that are more susceptible to groundwater contamination. To estimate advective particle tracking, we developed a 3D flow model using VisualModflow and MODPATH to describe the regional flow of groundwater. The vulnerability map demonstrates the problematic application and interpretation of the qualitative vulnerability method of the parametric system group, which indicates a difference of approximately 23% when compared with the modified vulnerability map. Potential contamination sources based on landfill sites were comparatively high; approximately 76% are located in areas that could be susceptible to contamination through vertical infiltration, especially those that are located along the Lerma system of wells. Industrial parks located in the centre of the valley (83%), where continuous extraction of groundwater and land subsidence occurs, have been classified as high vulnerability zones, increasing the risk of contaminants from surface sources reaching the groundwater. In order to understand the susceptibility to contamination in the aquifer, various delineation approaches should be adopted and all the results that validate each other should be considered, thus making a good strategy for implementing different degrees of protection measures.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".