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Record W4408033724 · doi:10.1021/acssusresmgt.4c00457

Capillary Suction for Dewatering Oil Sands Mature Fine Tailings: Experimental and Modeling Results

2025· article· en· W4408033724 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Sustainable Resource Management · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDewateringTailingsOil sandsSuctionPetroleum engineeringCapillary actionEnvironmental scienceGeotechnical engineeringGeologyMaterials scienceMetallurgyEngineeringComposite material

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The main challenge to the efficient processing of surface-mined oil sands is the generation of large volumes of slow-settling mature fine tailings (MFT). Tailings pose a significant environmental risk, including failure of storage facility, air-borne emissions, and surface and groundwater contamination. There is no practical mechanism to economically, technically, and ecologically dewater tailings to sufficiently dry MFT for reclamation. In this work, we leverage the natural mechanism of capillary action over filter paper to increase the area available for evaporation. The filter paper accelerated the evaporation of ∼35% of the MFT water, leading to an ∼20% increase in solidification and 33–55% reduction in reclamation time. The thermodynamics of water evaporation and mass transfer are modeled to estimate the rate of evaporation. Model fitting shows good accuracy in estimating dewatering for both nonporous and porous substrates using one and two fitted parameters, respectively.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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