Groundwater vulnerability assessment with using GIS in Hamadan–Bahar plain, Iran
Bibliographic record
Abstract
Abstract Vulnerability assessment to delineate areas that are more susceptible to contamination from anthropogenic sources has become an important element for sensible resource management and land use planning. It has been recognized for its ability to delineate areas that are more likely than others to become contaminated as a result of anthropogenic activities near the earth’s surface. The main methods of mapping and assessing intrinsic vulnerability in porous media are the following: SI, GOD, SINTACS and DRASTIC. The basic purpose of these maps is to divide an area into more classes, each of which will represent a different dynamic for a specific purpose and use. These models have been used to map groundwater vulnerability to pollution in Hamadan–Bahar aquifer. The results showed in models of DRASTIC, SI, GOD and SINTACS, respectively, 7.1, 44.21, 29.56 and 20.16 percent of the areas are high potential vulnerabilities. According to the model DRASTIC at study area, 33.6% of has a low class of groundwater vulnerability to contamination, whereas a total of 29.4% of the study area has a moderate vulnerability. The final results indicate that the aquifer system in the interested area is relatively protected from contamination on the groundwater surface. The correlation between models shows that DRASTIC model has the highest CI, which is 141, and the GOD model has the highest CI, which is 139. Also, the highest CI for SINTACS and SI is 137 and 136, respectively. Therefore, DRASTIC model is the best model among these models for predicting groundwater vulnerability in Hamadan–Bahar plain aquifer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".