The VISHMOD Methodology with Hydrochemical Modeling in Intermountain (Karstic) Aquifers: Case of the Sierra Madre Oriental, Mexico
Bibliographic record
Abstract
Hydrogeochemistry can be studied qualitatively using graphics such as scatter plots and Piper, Durov, and Schoeller diagrams, among others, and quantitatively by applying mass balance mixing models. The VISHMOD methodology (Virtual Samples in Hydrochemical Modeling) combines these two forms of hydrogeochemical characterizations. It is performed by applying hydrogeochemical modeling to virtual samples. This method makes standardization and control possible in order to demonstrate the extent to which a model is able to reproduce field measurements. Therefore, hydrogeochemical models of hydrogeological systems must be calibrated. This methodology was applied to carbonate and homogeneous media in the Sierra Madre Oriental in Mexico. Using the VISHMOD methodology in this region resulted in the classification of the water type as calcium bicarbonate (Ca-HCO3), representing a ternary mixture in which 45.5% was associated with local flow, 38.5% to intermediate flow and 16.5% to water-rock interaction. The main mineral phases were saturated calcite and sub-saturated dolomite, both from limestone contained in the Tamaulipas Formation.
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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 | 0.000 | 0.000 |
| 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".