Assessment of self-weight consolidation of flocculated fluid fine tailings under various environmental conditions
Bibliographic record
Abstract
Self-weight consolidation properties of polymer-flocculated fluid fine tailings (FFT) have been investigated in a laboratory using a meso-scale column apparatus with dimensions of 300 mm (diameter) by 1.8 m (height). Four meso-scale columns were used to carry out self-weight consolidation of flocculated FFT with flocculant dosages of 650 g/L (one column) and 850 g/L (three columns). The laboratory investigation attempts to mimic set-ups of the meso-scale consolidation columns under various environmental considerations such as with no evaporation (one column of 850 g/L dosage), with evaporation only (one column of 650 g/L and one column of 850 g/L dosage), and with evaporation and decantation (one column of 850 g/L dosage). The laboratory investigation enables the characterisation of self-weight consolidation of flocculated FFT, using the relationship between the total settlement of deposited tailings versus measured excess pore water pressure dissipation and expected hydrostatic line for various flocculant dosages and environmental considerations. The measured data will be used to assess and verify the self-weight consolidation properties with respect to the principles of soil mechanics.
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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".