A project report presented to the University of Waterloo in fulfilment for the degree of Master of Science in Earth Science, supervised by Walter Illman
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.267 | 0.078 |
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".