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Record W2802428010 · doi:10.1002/cjce.23099

A review of the physicochemical properties and flotation of pyrrhotite superstructures (4C – Fe<sub>7</sub>S<sub>8</sub>/ 5C – Fe<sub>9</sub>S<sub>10</sub>) in Ni‐Cu sulphide mineral processing

2017· review· en· W2802428010 on OpenAlexafffundvenue
Ravinder S. Multani, Kristian E. Waters

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPyrrhotiteSuperstructureGangueConfusionMineralMaterials scienceReagentMineralogyGeologyChemistryMetallurgyPhysical chemistryPyrite

Abstract

fetched live from OpenAlex

Abstract Pyrrhotite is one of the most abundant sulphide gangue minerals in base metal mining operations. It has little economic value and is a significant contributor of SO 2 emissions at the smelting stage, thus it is rejected in most concentrators, requiring a concerted effort to ensure it is well separated from valuable minerals. The mineral processing of pyrrhotite is generally understood, however it has many complex crystallographic structures (superstructures) that behave differently in the flotation pulp for which the information is sparse and dispersed throughout the literature. Traditionally, the superstructures were largely isolated into magnetic/non–magnetic circuits and floated separately. Many operations have now transitioned away from magnetic separators, and treat the superstructures in the same flotation circuit where their flotation responses are quite different. Historically, it was perceived that the superstructures behaved virtually the same; whereas, recent work has clearly demonstrated that there are quantifiable differences in their flotation responses. The available information is scattered in the literature and is also plagued with many contradictions, making it very difficult for researchers to retrieve relevant information pertaining to the superstructures. This review aims to reduce the confusion by summarizing the superstructure nomenclature, characterization methods, aqueous stability, surface charges and superstructure‐reagent interactions, and available flotation data.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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 teacher head, not a consensus.

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

Citations55
Published2017
Admission routes3
Has abstractyes

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