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Record W2810287053

Fluorspar: : Year in Review 2016

2016· article· en· W2810287053 on OpenAlexaboutno aff
Kasia Patel

Bibliographic record

VenueIndustrial Minerals · 2016
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural economicsQuarter (Canadian coin)PurchasingChinaTonneEconomicsSupply and demandBusinessNatural resource economicsGeographyOperations managementEngineeringWaste managementMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Steel market demand for metallurgical fluorspar (metspar) remained soft, despite an upturn in steelmaking globally from the second quarter, while capacity closures were offset by new production coming online in Asia and South America. PRICE TRENDS Prices for both acidspar and metspar began the year at low levels, due to a combination of oversupply and weak end market demand as a result of the slowdown in global construction markets. [...]acidspar prices from Mexico remained mostly stable in the first three months of this year, mainly because of limited trading activity on the back of overcapacity in the fluorochemicals sector and a lack of purchasing activity in hydrofluoric acid (HF) and AlF3 markets. While FOB China prices did rise to around $280/tonne in the second quarter due to heavy rain affecting supply and some renewed demand, new supply from Southeast Asia and high inventories in the fluorine supply chain stymied further...

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.374
Threshold uncertainty score1.000

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)0.0010.001

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 head0.051
GPT teacher head0.273
Teacher spread0.222 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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