Hydrogeophysics data acquisition for the characterization of hydraulic properties of the Vars-Winchester esker, southeastern Ontario
Bibliographic record
Abstract
A general workflow is proposed to characterize hydraulic properties of unconsolidated aquifers from geophysical data. Three-dimensional hydraulic property models are essential for groundwater flow and solute transport studies aimed at assessing water resources sustainability. Spatial coverage offered by conventional hydraulic characterization approaches based on hydraulic tests in wells are; however, often limited due to the time required to perform hydraulic tests, especially when high-resolution and/ or large complex areas need to be characterized. On the other hand, geophysical surveys can reveal important architectural features of an aquifer system; however, the quantification of geophysical attributes into hydraulic properties is difficult to achieve under field conditions. To overcome those limitations, a hydrogeophysical approach based on the definition of site-specific relationships between collocated geophysical and hydraulic data is proposed to allow the translation of geophysical attributes into hydraulic properties. The development and application of this approach that is currently underway for the Vars-Winchester esker system in south-east Ontario is reported here.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".