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Record W3193490791 · doi:10.1016/j.clema.2021.100009

A calculator for valorizing bauxite residue in the cement industry

2021· article· en· W3193490791 on OpenAlexaff
Michael Di Mare, Valerie Nattrodt Monteiro, Victor Brial, Claudiane Ouellet‐Plamondon, Sébastien Fortin, Katy Tsesmelis, Marcelo Montini, Diego Rosani

Bibliographic record

VenueCleaner Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsRio Tinto (Canada)École de Technologie Supérieure
Fundersnot available
KeywordsBauxiteResidue (chemistry)Raw materialCementWaste managementPortland cementCalculatorBusinessEngineeringComputer scienceMaterials scienceMetallurgyChemistry

Abstract

fetched live from OpenAlex

A computational tool using Microsoft Excel was developed to identify opportunities to repurpose bauxite residue as a raw material in the production of Portland cement . The tool quantifies the value of utilizing BR in this manner in terms of economic and environmental factors, including on-site and off-site electricity production and carbon taxes. This enables the tool to provide an optimization of the quantity of bauxite residue to be used based on the user’s specifications. The algorithm considers valorization of bauxite residue separately as both an ingredient in the raw meal and a supplementary cementitious material to maximize the opportunities to utilize the residue. The tool is designed to be used by users of both the alumina and cement industries and is compatible with the needs of each sector to consider the costs of commercialization, transportation, and cost-advantages of valorizing bauxite residue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.248
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations7
Published2021
Admission routes1
Has abstractyes

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