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Record W2039139356 · doi:10.1080/17480930902843102

Revaluing mine waste rock for carbon capture and storage

2009· article· en· W2039139356 on OpenAlexaff
Michael Hitch, Sheila M. Ballantyne, Sarah Robin Hindle

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon sequestrationCommodityBeneficiaryCarbon capture and storage (timeline)Waste managementValue (mathematics)Natural resource economicsBusinessEnvironmental scienceEngineeringCarbon dioxideEconomicsGeologyClimate changeComputer science

Abstract

fetched live from OpenAlex

Many mining wastes, especially those from the metal mining industry, have traditionally been treated as a matter of little or no value and in practice a cost burden. Some wastes, because of their reactivity characteristics, have emergent values that go beyond purely economic and into the environmentally beneficial realm. This article discusses the changing paradigm of mine waste management. Such fundamental parameters, such as cut-off grade and strip ratio, are positively impacted by the revaluation of waste rock material. Mine rock waste can now be seen as a commodity of value similar to the underlying ore being mined, which influences the economic performance of suitable projects. This valuable material can be used for industrial purposes including acid neutralisation as well as the capture and long-term disposal of anthropogenic carbon dioxide. The Turnagain Nickel project, located in Northern British Columbia, serves as an example of how waste rock material can become an important matrix for carbon capture and sequestration and how a project can benefit economically from it. By producing material that has emergent economic value as well as carbon capture and sequestration capabilities, the sequestration matrix producer is a beneficiary of new carbon credits that have a market value and is looked upon favourably by regulatory authorities and affected stakeholders.

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 score0.377
Threshold uncertainty score0.261

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations97
Published2009
Admission routes1
Has abstractyes

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