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Record W2948570671

Argex eyes global market

2013· article· en· W2948570671 on OpenAlexaboutno aff
Siobhan Lismore-Scott

Bibliographic record

VenueIndustrial Minerals · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)ShoreAffect (linguistics)BusinessCommerceAdvertisingGeologyManagementEconomicsSociologyOceanographyCommunication
DOInot available

Abstract

fetched live from OpenAlex

The quantity of the iron can affect the colour - Chinese producers tend to use a lower grade TiO 2 ore and that contains a lot of iron, CEO Roy Bonnell told IM. We take the stuff which has impurities that others cannot use, Enrico Di Cesare, COO and VP of technology at Argex, told IM. We have a couple of properties on the north shore of Quebec, which gives us the flexibility to develop our own properties if we want to do so, Di Cesare agreed, adding: That comes with the expense and headache of opening and operating our own mine. We are not looking to do that in the immediate future.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.996

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)0.0130.005

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 head0.038
GPT teacher head0.212
Teacher spread0.174 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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