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Record W4206341808 · doi:10.1139/cgj-2021-0356

Compressibility and permeability of sand–silt tailings mixtures

2022· article· en· W4206341808 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsTailingsPermeameterGeotechnical engineeringConsolidation (business)CushioningHydraulic conductivitySiltPermeability (electromagnetism)Void ratioGeologyCompressibilityMaterials scienceSoil waterSoil scienceComposite materialChemistryMetallurgyEngineering

Abstract

fetched live from OpenAlex

Microstructure showing the involvement of the fine and coarse grains in the soil skeleton is evaluated. Incremental loading tests using a stress-dependent permeameter are conducted on the mixtures of poorly graded sand and nonplastic fines originating from tailings. The results are compared with the published data of various tailings. It is shown that increasing the fines content from 0% to 100%, the involvement of the fine and coarse components of soil skeleton can be classified into four categories: no fines involvement (<10% fines), fines partially involved (10%−35% fines), increasing cushioning effect surrounding the coarse (35%−40% fines), and constant cushioning effect (>40% fines). At the same consolidation stress, the void ratio, e, rapidly decreases for fines less than 30%, then almost remains constant between 30% and 50% fines, and gradually increases for fines exceeding 50%. The hydraulic conductivity, k, decreases more than 20-fold as the fines content increases from 12% to 50%, then remains constant. k is proportional to [e 3 /(1 + e)] A and inversely proportional to S 2 , where A is a factor describing the effect of particle angularity and S is the specific surface. Finally, the influence of fines content on the seepage-induced internal stability is discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.444

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 integrity0.0000.001
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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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