Bibliographic record
Abstract
In general, tailings dams are expected to seep. Anomalous seepage, especially when induced by internal erosion, is a major concern for owners and operators. The long established techniques for monitoring water seepage provide sparse information which may not be sufficient to detect and map the seepage path. Hence, there exists a great need for non-invasive techniques that would be sensitive to changing seepage conditions. The non-invasive nature of the techniques is particularly important because drilling and other penetrating (invasive) investigation methods are normally avoided. Non-invasive techniques such as self-potential and high-resolution resistivity have been significantly improved in the past decade and have been successfully used for water retention dam investigation and monitoring. The main difficulty in the use of these techniques in monitoring sulfide rich tailings dams is the presence of electrochemical potentials that renders the interpretation of the acquired self-potential data difficult. Numerical modelling is one of the latest methods in interpreting self-potential anomalies induced by liquid flow. But, in order to model streaming potentials several parameters need to be measured or estimated; (1) the hydraulic driving force and the hydraulic conductivity are required to solve for the hydraulic pressure distribution; (2) the cross-coupling conductivity distribution is needed to calculate the conduction current source parameter; and (3) the resistivity distribution is needed to determine the resulting potential distribution. The zeta-potential and the resistivity of three pyrite rich tailings from the Abitibi region in Quebec were measured over the pH range 2 to 5 in different KCl aqueous solutions for the purpose of estimating the magnitude of electrokinetic effect induced by mine water seepage and the electrical resistivity variation induced by particle migration. The experimental and theoretical results obtained in the present study are pertinent to the interpretation of self-potential data. The zeta-potential was found to vary from -27 to -2 mV and the resistivity of the tailings was found to increase when fine particles are eroded.
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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 | 0.000 | 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) | 0.001 | 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".