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Record W4416305495 · doi:10.1039/d5cp03347k

Characterization of the sedimentation and drying processes of complex mining tailings materials using NMR

2025· article· en· W4416305495 on OpenAlexaff
SeyedHamed Derakhshandeh, Jussi Nousiainen, Joonas Karvo, Saija Luukkanen, Pertti Sarala, Ville‐Veikko Telkki, Elena Kozlovskaya, Владимир В. Живонитко

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsAgnico Eagle (Canada)
FundersOulun YliopistoKvantum-instituutti, Oulun YliopistoHORIZON EUROPE Framework ProgrammeAcademy of FinlandEuropean Commission
KeywordsTailingsSedimentationCharacterization (materials science)SettlingAerationProcess (computing)DecantationRelaxation (psychology)

Abstract

fetched live from OpenAlex

relaxation times. It allows one to quantify the water content during the transition from slurry-like to sludge-like forms of the studied tailings. The findings can provide valuable tools to characterize tailings sedimentation and dewatering, highlighting the potential of portable NMR as an analytical tool for real-time monitoring and optimization of TSF management strategies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.015
GPT teacher head0.314
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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