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Record W3047459021 · doi:10.3390/min10080692

Production of a Phosphate Concentrate from the Tailings of a Niobium Ore Concentrator

2020· article· en· W3047459021 on OpenAlexaff
Anthony Clapperton, Claude Bazin, Dominic Downey, Jean-Sébastien Marois

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTailingsApatitePhosphateNiobiumLeaching (pedology)PhosphoritePhosphate fertilizerEnvironmental scienceMetallurgyChemistryMineralogyMaterials science

Abstract

fetched live from OpenAlex

Apatite is the main source of phosphorous for the making of chemical fertilizers. While apatite is usually recovered from phosphate orebodies as the primary product of a mining exploitation, this paper documents the approach taken to produce a phosphate concentrate as a secondary product from the tailings of a niobium ore concentrator. The conventional desliming/flotation scheme used to process phosphate ores was tested and adapted to process one of the reject streams of a niobium concentrator in order to produce a salable phosphate concentrate. For that particular material, it was found that the reverse flotation of apatite yielded better results than the commonly used direct flotation of apatite. The recommended approach to produce the phosphate concentrate is desliming followed by reverse flotation of apatite and an acid leaching of the apatite concentrate to lower the MgO content below the specification for a phosphate concentrate. The obtained phosphate concentrate assays more than 32% P2O5 at a P2O5 recovery of 41%, which although low is found to be economic for the case of processing plant reject tailings.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.242
Teacher spread0.221 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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