Modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment in the southern Tehran aquifer
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to modify the SINTACS and DRASTIC models with a land-use (LU) layer and compares the modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment (GVA) in the southern Tehran aquifer, Iran. Single parameter sensitivity analysis (SPSA) served to determine the most significant parameters for the modified-DRASTIC, modified-SINTACS and SI approaches, and to revise model weights from "theoretical" to "effective." The inherent implementation of LU in the SI model may explain its better performance compared to unenhanced versions of DRASTIC and SINTACS models. Validation of all models, using nitrate concentrations from 20 wells within the study area, showed the modified-SINTACS model to outperform other models. The SPSA showed that the vadose zone and LU strongly influenced the modified-DRASTIC and modified-SINTACS models, while SI was strongly influenced by aquifer media and LU. To improve performance, models were implemented using "effective" instead of "theoretical" weights. Model robustness was assessed using nitrate concentrations in the aquifer and the outcomes confirmed the positive impact of using "effective" versus "theoretical" weights in the models. Modified-SINTACS showed the strongest correlation between nitrate and the vulnerability index (coefficient of determination = 0.75). Application of the modified-SINTACS while using "effective" weights, led to the conclusion that 19.6%, 55.2%, 23.4%, and 1.6% of the study area housed very high, high, moderate and low vulnerability zones, respectively.
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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 | 0.006 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it