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Record W2901217242

Tight supply of high-grade titanium feedstock to impede pigment production in Europe

2018· article· en· W2901217242 on OpenAlexaboutno aff
Cameron Perks

Bibliographic record

VenueIndustrial Minerals · 2018
Typearticle
Languageen
FieldChemistry
TopicPigment Synthesis and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsRutileEconomic shortageTitaniumRaw materialProduction (economics)Titanium dioxideNatural resource economicsBusinessEnvironmental scienceMaterials scienceMetallurgyEconomicsEngineeringChemistryChemical engineering
DOInot available

Abstract

fetched live from OpenAlex

Production of rutile and titanium dioxide slag has gone down, with potentially serious consequences for chloride output in Europe. Pigment producers in Europe are facing a shortage of the rutile and titanium slag feedstocks needed for making high-grade titanium, after a decline in supplies of both materials from Australia, Canada and South Africa. The tightened supply of rutile largely reflects a decrease in output from Iluka’s closed Murray Basin operations in Australia, while availability, current prices and contracts are already being affected by Tronox’s announcement that it will remove around 20,000 tonnes per year of rutile and leucoxene from the market by the end of 2018. Additionally, the imminent closure of Sibelco’s Stradbroke Island mine in Australia will remove as much as 35,000 tpy of rutile from the market by 2020. The recent supply tightness has triggered a rise in rutile prices for the third quarter of 2018.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.057
GPT teacher head0.257
Teacher spread0.200 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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