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Record W4324057415 · doi:10.1680/jenge.21.00038

Effect of wetting–drying cycles on the desiccation of tailings pre-dewatered with super absorbent polymer

2023· article· en· W4324057415 on OpenAlexaff
Anis Roshani, Mamadou Fall, Kevin Kennedy

Bibliographic record

VenueEnvironmental Geotechnics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWettingDewateringTailingsKeroseneMaterials scienceComposite materialContact anglePorosimetryChemistryPorosityGeotechnical engineeringMetallurgyPorous mediumGeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.213
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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