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Record W2116797544 · doi:10.2138/rmg.2006.61.7

Phase Equilibria at High Temperatures

2006· article· en· W2116797544 on OpenAlexaff
Michael E. Fleet

Bibliographic record

VenueReviews in Mineralogy and Geochemistry · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsWestern University
Fundersnot available
KeywordsCitationLibrary scienceComputer scienceIconDownloadChemistryInformation retrievalWorld Wide Web

Abstract

fetched live from OpenAlex

The wide variety of metal sulfide structures and their accommodation of atomic substitution, non-stoichiometry and metal-metal (M-M) and ligand-ligand interactions allows for diverse physical, chemical and electronic properties. The energy band structure of 3d transition-metal sulfides, in particular, is strongly influenced by the covalence of metal-S bonds, which results in hybridization of S 3p and metal 3d bonding states and direct or indirect M-M bonding interactions in favorable cases. Differences in the phase relations of isostructural metal sulfides are often attributable to subtle changes in electronic states. The literature on metal sulfide phase relations relevant to the earth sciences is very extensive and could not possibly be summarized in a single chapter. Therefore, following Craig and Scott (1974), this chapter focuses on the base metal (Fe, Co, Ni, Cu, and Zn) sulfides, with the literature for other metal chalcogenides and pnictides and some sulfosalts summarized in a single table (Table 1⇓). The relevant phase relations of the platinum-group element (PGE) chalcogenides and pnictides have been comprehensively reviewed in Makovicky (2002). A section on the halite structure sulfides (niningerite, alabandite and oldhamite) is also included in this chapter. The material presented here relates closely to that discussed in other chapters, in particular the chapter on sulfide thermochemistry (Sack and Ebel 2006). The importance of understanding phase equilibria in the context of electronic and magnetic properties and, hence, electronic structure is also emphasized, thereby reinforcing material presented in several other chapters (Pearce et al. 2006; Vaughan and Rosso 2006).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.005

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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
GenreReview

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

Citations158
Published2006
Admission routes1
Has abstractyes

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