Effect of wetting–drying cycles on the desiccation of tailings pre-dewatered with super absorbent polymer
Bibliographic record
Abstract
The main objective of this investigation is to understand the desiccation behaviour of fluid fine tailings (FFT) pre-dewatered with a superabsorbent polymer (SAP) with respect to wetting–drying cycles. Raw FFT (control) and pre-dewatered FFT with SAP were subjected to five cycles of wetting and six cycles of drying. Evaporation was found to be the dominant mechanism in the dewatering of raw FFT, as well as FFT pre-dewatered with SAP. In both cases, the suction developed in each drying cycle was eliminated temporarily by the succeeding wetting cycle. The minimum solids content within the column was found to be around 70%, which corresponded to a vane shear strength of 4.5 kPa. Based on the digital images of the surface, the crack intensity factors of the raw FFT and pre-dewatered FFT were calculated to be 26 and 14%, respectively. Scanning electron microscopy and mercury intrusion porosimetry analyses indicated that the FFT pre-dewatered with SAP after 25 days of evaporation in the second lift exhibited the most compacted texture. The solids content of the FFT pre-dewatered with SAP increased to 98% from an initial 50%. The SAP pre-dewatering method can significantly increase the shear strength of FFT subjected to wetting–drying cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".