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Record W4391025549 · doi:10.1680/jadcr.23.00195

Utilisation of local raw materials and mine waste to manufacture cement in the Northwest Territories, Canada

2024· article· en· W4391025549 on OpenAlexaffabout
Guangping Huang, Jian Zhao, Gideon Lambiv Dzemua, Scott Cairns, Philippe Normandeau, Wei Victor Liu

Bibliographic record

VenueAdvances in Cement Research · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsGovernment of Northwest TerritoriesUniversity of Alberta
Fundersnot available
KeywordsCementTailingsRaw materialCarbon footprintPortland cementEnvironmental scienceWaste managementClinker (cement)Mining engineeringMetallurgyGeologyGreenhouse gasEngineeringMaterials science

Abstract

fetched live from OpenAlex

Currently, all the cement consumed in the Northwest Territories (NWT), Canada, is imported from other provinces (e.g. Alberta) by long-distance (∼1800 km) freight truck. Transporting cement over long distances not only raises its cost, but also results in a higher carbon footprint. Producing cement locally is therefore a potential low carbon and economic solution for the local industry. However, it is unknown if the local raw materials are suitable for cement manufacture, and there is a lack of a critical raw material – iron ore – for cement manufacturing. However, instead of iron ore, there are iron-rich tailings from a local rare earth element (REE) mine. Towards a low carbon and circular economy, the use of local raw materials (i.e. limestone, clay and gypsum) and mine waste (REE tailings) to manufacture cement in the NWT was explored and the first bag of cement in the history of the NWT was produced. Concrete samples made with the NWT cement achieved strength comparable to that of concrete based on commercial ordinary Portland cement. In addition, it was estimated that locally producing cement in the NWT has the potential to reduce carbon dioxide emissions by 3.0–61.7% as compared with importing cement from other provinces.

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 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 score0.575
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.314
Teacher spread0.293 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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