Electrical Resistivity Imaging Revealed the Spatial Properties of Mine Tailing Ponds in the Sierra Minera of Southeast Spain
Bibliographic record
Abstract
Abstract Mine tailing ponds are environmental hazards because of high susceptibility to leaching and erosion by water and wind. Vegetation establishment is an effective technique to reclaim tailing ponds but requires knowledge of the spatial relationship between the structural composition and physical and chemical properties of soils. In this study we have demonstrated the use of electrical resistivity imaging (ERI), combined with soil chemical analyses, to determine the structural and chemical composition of mine tailing ponds to assess efficient measures of environmental protection. We used a Syscal R1 resistivity meter to generate two- and three-dimensional (2-D/3-D) ERI images from El Lirio and Brunita mine tailing ponds. Soil samples were collected at 1-m intervals to a depth of 15 m, and were analyzed for pH, electrical conductivity and cadmium (Cd), copper (Cu), lead (Pb) and zinc (Zn) contents. Results show that materials in the ponds can be classified into three categories: fine tailings – low ER (<8 Ω-m), coarse waste rock – intermediate ER (8–150 Ω-m), and bedrock – high ER (>150 Ω-m). Our interpretation of the 2-D/3-D ERI images with respect to the historical depositions of materials in the ponds show that at El Lirio, decant water outlet was initially at the center and advanced to the east of the tailing pond as the mining activities progressed. At Brunita, the intermediate ER values on the west side of the pond marked the deposition of coarse waste rock materials released during a pond breakage in 1972. The ERI helped us image the spatial distribution of tailings and its qualitative spatial correlation with chemical properties (i.e., pH, EC, metals content). Low ER values are related to high amounts of Zn, Pb, Cu and Cd. These qualitative relationships underlie the usefulness of the combined geophysical and soil chemical approaches to improve our understanding of the properties of mine tailing ponds in the Sierra Minera (and other parts of the world).
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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 | 0.000 | 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".