Comprehensive Geophysical Data Integration and Stratigraphic Contacts Delineation in a Regional Hydrogeological Characterization Study
Bibliographic record
Abstract
Abstract Accurate inference of the interfaces between geological units showing different hydraulic properties is a key step for a reliable hydrogeological characterization of regional aquifers. In this study, we developed a workflow that combines multiple geological and geophysical data sets having different intrinsic resolution to map a stratigraphic interface of the regional aquifer located in Montérégie, Quebec, Canada. One of the principal goals was to optimally assimilate all the data at all stages in the workflow. Firstly, the experimental variogram showed two structures of different ranges: one coming from highly sampled geophysical data and the other from conventional borehole geological markers. Secondly, a secondary variable is constructed with all secondary data (e.g., geological interpretations of low-resolution electromagnetic surveys), each having its own accuracy and resolution. To account for the variable secondary data, different reliability indexes were assigned as weights in a discrete smooth interpolation (DSI). Thirdly, a classic kriging with an external drift (KED) operator was used to interpolate the more reliable well data on the entire region. The approach was tested on the estimation of the bedrock interface elevation in a regional hydrogeological characterization study. The resulting map shows bedrock elevations coherent with geological structure of the region, representing main features such as outcrops and valleys. A cross section is presented to illustrate the philosophy behind the tools employed to achieve the estimation process. It also shows an example of visual quality control undertaken to validate the workflow.
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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 | 0.001 | 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 | 0.000 | 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".