Modification of the DRASTIC model to assess change in groundwater vulnerability over time
Bibliographic record
Abstract
Globally both the quantity and quality of groundwater has been degrading. For cities relying exclusively on groundwater, it is vital to have an accurate and cost-effective tool in order to plan and protect these aquifers. Using geospatial data (land use, digital elevation, soil mapping, Quaternary geology, aquifer/aquitard elevations, historical rainfall, watershed boundaries) in combination with an overlay-index method known as the DRASTIC model, areas of higher groundwater vulnerability that are considered to be more susceptible to contamination were identified. This research presents an approach utilizing a Geographic Information System (GIS) to compute a vulnerability analysis for the Alder Creek watershed west of Kitchener-Waterloo, Ontario, Canada. This study investigates the change in groundwater vulnerability over a 54-year period, analyzing the land use change and its effects on the groundwater quality. This approach was able to conclude that anthropogenic influence over the watershed was not impactful enough to create an increase in groundwater vulnerability over time.
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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 | 0.001 |
| 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 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".