Combined application of surface geoelectrical methods for groundwater-flow modeling: A case history
Bibliographic record
Abstract
Abstract Electrical methods [electrical profiling and sounding, induced polarization (IP), and self-potential (SP)], combined with geochemical and hydrochemical surveys, were applied to characterize shallow sediments at a site contaminated by oil-related brines. The site is located within the Romashkinskoye oil field (Tatarstan Republic, Russian Federation). Results of geochemical and hydrochemical surveys, as well as those of electrical profiling, revealed no individual haloes of contaminant in the soil and groundwater, which suggests that the entire site is contaminated. Vertical electrical IP soundings (VES-IP) allowed us to obtain the stratigraphic characteristics of the site and to distinguish a shallow aquifer among clayey aquitards. Interpretation of SP data on the basis of forward numerical modeling helped us to develop a groundwater-flow model of the site. To reduce ambiguities, we kept fixed the values of layer resistivity derived from the inverted VES-IP data. Boundary conditions on water head and water flow were based on drilling and sampling data. We varied only the values of hydraulic conductivity to fit the calculated SP values to the measured SP values. We found a strong misfit between the measured and calculated SP values for the one-aquifer model. By inferring a second, deeper-seated aquifer, we computed a two-aquifer model that produced a satisfactory SP fit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".