Natural Vulnerability Assessment to Contamination of Unconfined Aquifers by Longitudinal Conductance – (S) Method
Bibliographic record
Abstract
Given the importance of the groundwater for diverse uses, particularly the public supply, and considering the increasing impacts on underground reserves as a result of overexploitations, as well as the degradation of the water quality by anthropogenic activities, it becomes essential to establish tools for planning and management of the use of groundwater resources. For this reason, the aim this paper is to assess the natural vulnerability to contamination of the Bauru Aquifer System, in the South-Central region of the State of São Paulo, Brazil. Therefore, a vulnerability map was generated by using the (S) method, a new proposal developed to estimate the vulnerability by means of the Dar Zarrouk parameter - longitudinal conductance (electrical resistivity method). The vulnerability classes were defined using values ranges of longitudinal conductance: from very low (> 2.5 siemens) to extreme (< 0.1 siemens). In applying this methodology in the unsaturated zone, the electrical resistivity of the first layer of the saturated zone was used, seeing that the materials of both zones are similar in the study area. The application of the (S) method produced good outcomes, generating a more detailed map, with greater classes variability that indicate the sensitivity of this method, predominating regions with low vulnerability, and secondly, moderately vulnerable areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".