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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".