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Record W2809838466

Canadian Wollastonite: : Developing a white mineral for green markets

2016· article· en· W2809838466 on OpenAlexaboutno aff
Industrial Minerals

Bibliographic record

VenueIndustrial Minerals · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsWollastoniteAgricultureGreenhouseAgricultural economicsBusinessChristian ministryEngineeringNatural resource economicsChemistryGeographyPolitical scienceAgronomyEconomicsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Canadian Wollastonite is privately-owned company, based in eastern Ontario, Canada. Incorporated in 2001 as 2005948 Ontario Ltd, the corporation operates under the trade name Canadian Wollastonite and describes itself as mining start-up business producing a white mineral for green world. Our short term focus has been on developing markets for run-of-mine ore products, [Bob Vasily] told IM. Our wollastonite ore grades are around 42-50% and 38-45% diopside. This ore is non-carbon emitting single mineral source of silicon (Si), calcium (Ca) and magnesium (Mg) and it is this chemistry that defines the markets we are selling into currently. include agriculture, horticulture, slag conditioning, animal feed and number of environmental applications. Research projects, many of which are being supported by grant from the Ontario Ministry of Agriculture, Food and Rural Affairs' Economic Development Programme, include testing Canadian Wollastonite's material in silicon-rich soils to increase pest and disease resistance in crops and to mitigate sodium build-up in greenhouse soils. It is also being investigated as product that can be used to remove phosphorous from greenhouse wastewater. These are markets that the large existing producers cannot really get into, due to their costs and limited resources, Vasily said.

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: Empirical · Consensus signal: none
Teacher disagreement score0.423
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)0.0050.000

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 head0.045
GPT teacher head0.270
Teacher spread0.225 · 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
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
Published2016
Admission routes1
Has abstractyes

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