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Record W2756371330

Thessally Resources view prospects in Australia's magnesite market

2014· article· en· W2756371330 on OpenAlexaboutno aff
Andrew Scogings, Matthew Barnett

Bibliographic record

VenueIndustrial Minerals · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMagnesiteDolomiteUltramafic rockCalcinationMineralogyTonneMagnesiumGeologyGeochemistryEnvironmental scienceChemistryMetallurgyMaterials scienceGeographyArchaeologyCatalysis
DOInot available

Abstract

fetched live from OpenAlex

Magnesite is the mineral name for MgCO[subscript]3 with a theoretical MgO content of 47.8% MgO and 52.2% CO[subscript]2. Deposits of magnesite are of two main types; (i) macrocrystalline or sparry rocks (Wilson, 2013) where Mg solutions have altered dolomite to magnesite, and (ii) cryptocrystalline magnesite replacing ultramafic rocks within the weathered profile. Other types of magnesite, such as sedimentary beds, crystalline magnesite replacement in ultramafic rocks and saline brines, do occur but are of secondary significance from an economic point of view. Also a pure form of magnesia (MgO) is produced from sea water. In 2012 USGS data shows world production capacity of CCM as 2.72m tonnes MgO and DBM as 7.64m tonnes MgO (Bray, 2012). Australia's share of this production capacity in 2012 was 8% CCM and 1.4% DBM. China dominates the supply of CCM with 53% of production capacity. Russia (3.4%), Spain (5.5%), and Brazil and Canada (3.5%) are other significant CCM producers, according to the USGS. As the main products from magnesite mines are in the calcined MgO form, this is how the product specifications are quoted. For CCM produced from natural magnesite the range of MgO is 85-95%, with 85-90% MgO being the typical range for animal feeds and fertilisers and 90-95% MgO for bulk industrial applications such as construction and paper processing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
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)0.0050.001

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 head0.050
GPT teacher head0.232
Teacher spread0.182 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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