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Record W4200138132 · doi:10.3390/min11121425

Titanium: An Overview of Resources and Production Methods

2021· article· en· W4200138132 on OpenAlex

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

VenueMinerals · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTitaniumIlmeniteProduction (economics)Materials scienceProcess (computing)MetallurgyProcess engineeringNatural resource economicsEnvironmental scienceComputer scienceEngineeringMineralogyChemistryEconomics

Abstract

fetched live from OpenAlex

For several decades, the metallurgical industry and the research community worldwide have been challenged to develop energy-efficient and low-cost titanium production processes. The expensive and energy-consuming Kroll process produces titanium metal commercially, which is highly matured and optimized. Titanium’s strong affinity for oxygen implies that conventional Ti metal production processes are energy-intensive. Over the past several decades, research and development have been focusing on new processes to replace the Kroll process. Two fundamental groups are categorized for these methods: thermochemical and electrochemical. This literature review gives an insight into the titanium industry, including the titanium resources and processes of production. It focuses on ilmenite as a major source of titanium and some effective methods for producing titanium through extractive metallurgy processes and presents a critical view of the opportunities and challenges.

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 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 score0.041
Threshold uncertainty score0.331

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)0.0000.000

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 head0.058
GPT teacher head0.355
Teacher spread0.297 · 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