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Extractive metallurgy of rare earths

2013· article· en· W2058338036 on OpenAlexaff
Fathi Habashi

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2013
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBeneficiationMonaziteIgneous rockLeaching (pedology)PrecipitationUraniumExtractive metallurgyGeochemistryMineralGeologyMetallurgyRare earthExtraction (chemistry)HydrometallurgyPhosphoriteMineralogyChemistryMaterials scienceCopperPhosphorusSoil water

Abstract

fetched live from OpenAlex

A short account is given on the extraction of rare earths from monazite sand, bastnasite ore, and phosphate rock of igneous origin. This includes mineral beneficiation, leaching methods, fractional crystallisation [of historical interest], ion exchange, solvent extraction, precipitation from solution, and reduction to metals.On donne un bref compte-rendu de l’extraction des terres rares à partir de sable monazité, de minerai de bastnaésite et de roche phosphatée d’origine ignée. Ceci inclut l’enrichissement du minéral, les méthodes de lixiviation, la cristallisation fractionnelle (d’intérêt historique), l’échange d’ion, l’extraction par solvant, la précipitation à partir de solution et la réduction en métaux.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 designNot applicable
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

Citations186
Published2013
Admission routes1
Has abstractyes

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