Identifying Preferential Acid Mine Drainage Flow Paths in the Shallow Subsurface using 3D Electrical Resistivity Tomography
Bibliographic record
Abstract
Summary The Catholic 40 is a reclaimed mining site located within the Tar Creek Superfund Site (TCSS) of Ottawa County, Oklahoma. Mining for lead and zinc ores took place in the early 20th century within the Mississippian-aged Boone Group, which consists of fractured limestone and interbedded chert. Large quantities of mining wastes and fine tailings were generated and disposed of on-site. Remediation efforts began in 2013 with the removal of surficial wastes (107,000 tons) and plugging of three mine shafts. Groundwater conditions at the site have not been assessed. Water sampled from mine shafts in other locations within the TCSS has been shown to have concentrations of cadmium as high as 590 μg/L, lead as high as 282 μg/L, and zinc as high as 560,000 μg/L as a result of weathering of sulfide minerals present in mine voids. Water contained in the underground mine workings is in direct contact with the groundwater in the surrounding rocks of the Boone Group and thus provides a source of direct contamination to the Boone aquifer. The objective of this study was to identify zones of low resistivity in the shallow subsurface at the Catholic 40 site in order to determine if mining-related contamination was migrating via preferential groundwater flow pathways. Electrical resistivity surveys are commonly applied to acid mine drainage studies because the products of acid mine drainage (SO42−, H+, metal ions) cause affected waters to be highly conductive to electrical current. 3D inversion results indicated a linear, low conductivity anomaly with an azimuthal orientation of 120° that extended vertically through the entire inverted volume. This zone of low conductivity was interpreted to be an open fracture through which potentially contaminated groundwater was moving.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".