Laboratory monitoring approaches for tailings settling and evaluation of flocculant and coagulant treatments
Bibliographic record
Abstract
Abstract X‐ray computer tomography (CT), magnetic resonance imaging (MRI), and nuclear magnetic resonance (NMR) are applied for laboratory monitoring of the settling behaviour in tailings suspensions. It is shown that the developed monitoring techniques are consistent with each other and provide different levels of information from 2D density cross‐sections obtained using X‐ray CT to 1D and bulk water content using MRI and NMR. Analysis of coagulant and flocculant treatments based on obtained measurement information is conducted using linear and nonlinear kinetic modelling approaches. The linear Roberts' kinetic model describes the settling of original tailings suspensions very well, but it is not adequate for the suspensions treated with additives. A modified nonlinear Roberts' model is developed and successfully applied to analyze the suspension with additives. The analysis of combined flocculants and coagulants treatments reveals that flocculants significantly affect the settling rate; however, they decrease the efficiency of settling with respect to the residual water content. Coagulants affect mostly the residual water content in samples, but do not change the settling rate significantly. The trade‐off between concentrations of coagulants and flocculants needs to be achieved for optimal settling treatments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".