MétaCan
Menu
Back to cohort
Record W4378907262 · doi:10.3390/min13060743

Agitation Leaching Behavior of Copper–Cobalt Oxide Ores from the Democratic Republic of the Congo

2023· article· en· W4378907262 on OpenAlexaff
Chaozhen Zheng, Kaixi Jiang, Zhanmin Cao, Derek O. Northwood, Kristian E. Waters, Haibei Wang, Sanping Liu, Kun’e Zhu, Hao Ma

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsMcGill UniversityUniversity of Windsor
FundersNational Key Research and Development Program of China
KeywordsLeaching (pedology)TailingsCobaltCopperOxideCobalt oxideChemistryMetallurgyHydrometallurgySulfideMetalCopper sulfideLixiviantCopper oxideNuclear chemistryMaterials scienceSulfuric acidGeology

Abstract

fetched live from OpenAlex

Agitation leaching is a promising technology in hydrometallurgy for treating copper–cobalt oxide ores. In this work, the behavior of oxide ores containing around 2.3% Cu and 0.3% Co received from Congo was investigated for varying particle size, acidity, pulp density, temperature, leaching time, and reduction potential. XRD, optical microscopy (OM), and ICP-OES methods were used to examine the chemical composition, morphology, and metal content of the samples. The copper and cobalt recovery reached 88.2% and 82.5%, respectively, at room temperature, with a leaching time of 4 h, a pulp density of 33%, an acidity of 178 g/L, and no reductant. The Cu and Co remaining in the leaching residue were found to be in their sulfide forms and coated with dense and fine calcium sulfate. To improve the metal recovery, a combination of flotation and agitation leaching of the flotation tailings method was adopted, after which the Cu and Co recovery reached 96.6% and 86.0%, respectively.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.188

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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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 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

Citations14
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMineralsSame topicMetal Extraction and BioleachingFrench-language works237,207