Transient Electromagnetic (TEM) Surveys as a First Approach for Characterizing a Regional Aquifer: The Case of the Saint-Narcisse Moraine, Quebec, Canada
Bibliographic record
Abstract
Geological contexts that lack minimal stratigraphic and piezometric information can be challenging to produce an initial hydrogeological map in remote territories. This study proposes an approach to characterize a regional aquifer using transient electromagnetic (TEM) surveys. Given the presence of randomly dispersed boreholes, the Saint-Narcisse moraine in the Mauricie region of Quebec (Canada) is an appropriate site for collecting the required geophysical data, correlating the stratigraphic and piezometric information, and characterizing regional granular aquifers in terms of stratigraphy, geometry, thickness, and extent. In order to use all TEM results (i.e., 47 stations) acquired in the moraine area, we also correlated 13 TEM stations, 7 boreholes, and 6 stratigraphic cross-sections to derive an empirical and local petrophysical relationship and to establish a calibration chart of the sediments. Our TEM data, combined with piezometric mapping and the sedimentary records from boreholes and stratigraphic cross-sections, revealed the compartmentalization of a multi-kilometer morainic system and indicated the presence of two large unconfined granular aquifers overlying the bedrock. These aquifers extend more than 12 km east to west across the study area and are between 25 and >94 m thick. The TEM method provides critical information on groundwater at a regional scale by acquiring information from multiple stations within a short time span to a degree not possible with other existing methodologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".