MétaCan
Menu
Back to cohort
Record W2514592920 · doi:10.1002/jrs.5011

Shell‐isolated nanoparticle‐enhanced Raman spectroscopy characterization of oxide ores during thiosulfate‐mediated gold leaching

2016· article· en· W2514592920 on OpenAlexafffund
Scott R. Smith, Janet Y. Baron, Yeonuk Choi, Jacek Lipkowski

Bibliographic record

VenueJournal of Raman Spectroscopy · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsBarrick Gold (Canada)University of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaBarrick Gold Corporation
KeywordsThiosulfateLeaching (pedology)Raman spectroscopyPolysulfideOxideChemistryMetalInorganic chemistryDissolutionNanoparticleElectrolyteSulfurMaterials scienceNanotechnologyElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

An applied approach for in situ characterization of oxide ore samples exposed to a thiosulfate‐based leaching solution with shell‐isolated nanoparticle‐enhanced Raman spectroscopy (SHINERS) is discussed. Differences in passive layer growth during thiosulfate leaching were observed in the SHINERS spectra between the untreated and pressure oxidation (POX)‐treated oxide ore samples received from Barrick Gold Corporation. The SHINERS spectra revealed that the passive layer at the untreated oxide ore–electrolyte interface contains metal sulfides and significant quantities of polysulfide chains of variable lengths after longer exposure to the leaching solution. However, the passive layer observed with the POX‐treated sample was found to be predominantly metal sulfides with only a small quantity of polysulfide chains. From these results, it was concluded that the POX pretreatment process may successfully destroy or inactivate minerals found in the ore that are responsible for catalysing thiosulfate decomposition into polysulfides which results in low gold extraction efficiencies. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.215
Teacher spread0.210 · 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 designBench or experimental
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

Citations3
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Raman SpectroscopySame topicMetal Extraction and BioleachingFrench-language works237,207