Characterization of Self-Weight Consolidation of Fine-Grained Mine Tailings Using Moisture Sensors
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Bibliographic record
Abstract
Abstract This paper presents an experimental procedure and testing results on the consolidation of saturated tailings based on measurements made with moisture sensors. A special calibration procedure has been developed for precise volumetric water content measurements to reflect the progressive change of density and void ratio in the loose tailings. The moisture sensors’ readings are used to evaluate key parameters during self-weigh consolidation, which allow an assessment of the settlement’s rate and magnitude. The results indicate that the proposed technique can be useful to assess the tailings slurry characteristics, which significantly evolve during self-weight consolidation. The experimentally determined parameters are in the range provided by other investigations conducted on the same materials. It is also shown that pore-water pressures deduced from the volumetric water content measurements during the laboratory tests correlate well with the profiles obtained from the one-dimensional consolidation theory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it