Assessing and Mapping Groundwater Vulnerability to Bacteria in Alberta
Bibliographic record
Abstract
Aquifer vulnerability index methods are commonly used for assessing groundwater vulnerability to surface contaminants. However, the methods have primarily been developed for dissolved contaminants. Microbial contaminants have unique characteristics that result in different transport behavior in the subsurface, and thus different tools need to be designed. Key vulnerability factors specific to microbial sources and subsurface transport mechanisms were identified in this study and incorporated into a model using an ArcGIS framework to create provincial-scale maps of groundwater vulnerability, specific to E. coli, in Alberta for the year 2012. Examples of these factors include: soil texture based on grain size, soil organic matter, hydrogeologic properties, depth to aquifer, and meteorological conditions. These factors were combined from individual GIS layers to create an intrinsic vulnerability map, demonstrating where aquifers were more vulnerable to bacterial contamination if a source became present. Maps were created for the growing season and cold season, and attempts were made to test the model with E. coli detection data. The results of these statistics were not significant enough for this model to be used for predictive purposes, but this could be caused by the presence or lack of risk (i.e., source of contaminants), as opposed to real differences in aquifer vulnerability. This project helped inform which factors should be considered when making a vulnerability map for bacterial contaminants, most notably temporal factors such as precipitation and soil moisture. The developed map provided insights as to where shallow aquifers in Alberta are intrinsically vulnerable to bacterial contamination.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".