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Record W4387736828 · doi:10.1080/19236026.2023.2251101

Axial or turn-by-turn particle recovery in a spiral concentrator

2023· article· en· W4387736828 on OpenAlex
Laurence Boisvert, Maryam Sadeghi, Christian M. Rochefort, Claude Bazin

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

VenueCIM Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversité LavalArcelorMittal (Canada)CIMA+ (Canada)
FundersFonds de recherche du Québec – Nature et technologies
KeywordsConcentratorSpiral (railway)Turn (biochemistry)PhysicsMathematicsEngineeringMineralogyMechanical engineeringOpticsChemistryNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Spiral concentrators (“spirals”) are commonly used to separate valuable heavy minerals from light gangue minerals by gravity. This paper examines the classification of particles as they flow down a spiral concentrator and relates the results to the number of turns. The tests show the possibility of reproducing the performance of industrial spirals with a spiral operating in a closed circuit in a laboratory. Results show that knowing the mineral size distributions in the spiral feed is necessary to forecast spiral performance. Further, in the case of iron ore processing, the separation process is practically complete after four turns, with wash water affecting the process rate of recovery. This observation is readily explained by considering the mineral size distribution.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
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)0.0070.001

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 head0.022
GPT teacher head0.283
Teacher spread0.260 · 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