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Grade Estimation At Cvrd Inco’S Canadian Sulphide Mines

2007· article· en· W2317527187 on OpenAlexaffabout
Glenn McDowell, Andrew D. Mackie, Mark Palkovits

Bibliographic record

Venue20th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems · 2007
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsVale (Canada)
Fundersnot available
KeywordsPyrrhotiteElectrical resistivity and conductivityConductivityEddy currentMaterials scienceMineralogyMetallurgyAnalytical Chemistry (journal)Environmental scienceGeologyChemistryElectrical engineeringEnvironmental chemistryPyriteEngineering

Abstract

fetched live from OpenAlex

The paper provides an overview of grade estimation at CVRD Inco Limited’s (CVRD Inco) Canadian sulphide mines. The main Ni grade estimation technique used is blasthole conductivity. This<br>technique is applicable in zones where the sulphide is sufficiently well connected to provide eddy current responses in inductive conductivity probes. In zones where the sulphide content is low (less than 25-30%) and the degree of sulphide connectivity is insufficient to cause reliable eddy current responses in inductive conductivity probes, magnetic susceptibility is being employed to enhance Ni grade estimation. In these zones, magnetic susceptibility complements conductivity-based grade estimation<br>because of the monoclinic or magnetic pyrrhotite content of the sulphide. Additional techniques being investigated for grade estimation and discrimination between chalcopyrite-rich and pyrrhotite-rich mineralization include resistivity, natural gamma and density. The paper concludes with a summary of the nuclear-based PGNA (Prompt Gamma Neutron Activation) technique. This is a more direct grade determination technique that employs a neutron generator and gamma detectors. The resulting gamma ray spectra contain information about elemental concentrations including Ni, Cu, Co, Fe, S, Si, Ca, Al, etc.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.669

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)0.0000.000

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 head0.005
GPT teacher head0.174
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2007
Admission routes2
Has abstractyes

Explore more

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