Assessing rock aquifer vulnerability using downward advective times from a 3D model of surficial geology: A case study from the St. Lawrence Lowlands, Canada
Bibliographic record
Abstract
An aquifer vulnerability assessment method which relates vulnerability directly to groundwater Downward Advective Time (DAT) from a 3D geologic model is tested over a 1400 km2 area in the St. Lawrence Lowlands in SW Québec. The goal was to assess the vulnerability of the regional rock aquifer which is overlain by discontinuous and unconsolidated Quaternary units. Hydrogeologic parameters such as groundwater recharge and porosity of each unit overlying the aquifer were integrated to a gOcad 3D stratigraphic grid. This grid was generated from a 3D geologic model made of interlocked surfaces representing the boundaries of Quaternary units and the underlying bedrock topography. Calculations were carried out using a deterministic approach and DAT estimates were obtained for 74% of the model area. Results are tentatively grouped into 6 DAT classes which are interpreted in terms of a relative vulnerability index. The spatial distribution of this index is in good agreement with the hydrogeological settings and available hydrogeochemical data. Results indicate that 40% of the evaluated area falls within the moderately high to very high vulnerability classes. Comparison between this map and a DRASTIC map of the same area reveals some significant discrepancies, particularly in areas of shallow depth to bedrock which are characterized by fast DAT through a thick unsaturated zone. In these areas DRASTIC produces low vulnerability scores while this assessment yields high vulnerability ranking. Overall, this assessment provides a sound basis for regional planning and decision making.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".