Multitechnique approach for characterizing the hydrogeology of aquifer systems: application to the Mauricie region of Québec, Canada
Bibliographic record
Abstract
The Groundwater Knowledge Acquisition Project (Programme d’acquisition de connaissances sur les eaux souterraines, PACES) in eastern Mauricie, Québec, aimed to develop multiple groundwater characterization methods and provided a more comprehensive portrait of this resource across the region. The proper management and protection of regional groundwater require an accurate characterization and detailed description of regional aquifers. Here, a comprehensive summary is presented, showcasing the amalgamation of specialized investigation tools and diverse groundwater characterization methods developed throughout the Eastern Mauricie PACES project. This holistic approach unveils valuable insights into hydrogeological units while identifying the crucial parameters that define the regional aquifers. Information related to groundwater surface or subsurface distribution is presented through comprehensive thematic maps, stratigraphic sections, 3D fence diagrams, novel geophysical techniques, conceptual models, and numerical modeling. Integrating a comprehensive spatially referenced database, targeted subregional studies, and peer-reviewed research has significantly enhanced our understanding of regional groundwater. This paper offers substantial information on regional aquifers and their surrounding geology. The approach developed during the eastern Maurice PACES project will serve to better manage and protect groundwater resources in Québec and elsewhere.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".