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Record W2796660662 · doi:10.3390/min8040147

Integrated Mineral Carbonation of Ultramafic Mine Deposits—A Review

2018· article· en· W2796660662 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMinerals · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsTrent University
FundersFundamental Research Funds for the Central UniversitiesChina Scholarship CouncilCarbon Management Canada
KeywordsCarbonationCarbon sequestrationTailingsEnvironmental scienceWaste managementMaterials scienceEngineeringMetallurgyChemistryCarbon dioxideChemical engineering

Abstract

fetched live from OpenAlex

Recently, integrated mineral carbonation for CO2 sequestration has received significant attention due to the high potential for commercialization towards mitigating climate change. This review compiles the work conducted by various researchers over the last few years on integrated mineral carbonation processes in the mining industry, which use ultramafic mine wastes as feedstock for mineral carbonation. Here, we introduce the basic concepts of mineral carbonation including a brief description of the process routes and pre-treatment techniques. We discuss the scope of integrated mineral carbonation process application, and critically review the integrated mineral carbonation process in the mining industry including modified passive carbonation techniques in tailing storage facilities, and ex-situ carbonation routes using fresh tailings. The focus of the discussions is the role of reaction condition on the carbonation efficiency of mine waste with various mineralogical compositions, and the benefits and drawbacks of each integrated mineral carbonation process. All discussions lead to suggestions for the technological improvement of integrated mineral carbonation. Finally, we review the techno-economic assessments on existing integrated mineral carbonation technologies. Research to date indicates that value-added by-products will play an important role in the commercialization of an integrated mineral carbonation process.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.992

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)0.0090.000

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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