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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 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.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.321

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 head0.021
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