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Record W2894681558 · doi:10.2166/wst.2018.405

Flocculation performance of anionic starch in oil sand tailings

2018· article· en· W2894681558 on OpenAlex
Nana Zhao, H. Bitar, Yunyin Zhu, Yuming Xu, Zhiqing Shi

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.

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsNatural Resources CanadaNational Research Council Canada
Fundersnot available
KeywordsTailingsFlocculationSettlingOil sandsFiltration (mathematics)SedimentationSedimentChemistryPulp and paper industryEnvironmental scienceMaterials scienceGeologyEnvironmental engineeringMetallurgyComposite materialEngineering

Abstract

fetched live from OpenAlex

A series of carboxymethyl starches (CMSs), with various degrees of substitution from 0.1 to 0.79, were synthesized and selected as a model to study the feasibility of using natural polymers as flocculants for oil sand tailings treatment. The flocculation performance of modified CMS in kaolin clay suspensions and oil sand tailings was evaluated in terms of settling rate, solids content, capillary suction time, and specific resistance to filtration of the sediment phase. It was found that the synthesized CMS effectively accelerates settling of kaolin suspensions and oil sand fine tailings, thus demonstrating the feasibility of this application.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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