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Record W3023375148 · doi:10.18280/ijsdp.150308

Engineering Properties of Bauxite Residue

2020· article· en· W3023375148 on OpenAlexvenueno aff
Anastasios Mouratidis, Panagiotis Nikolidakis

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsBauxiteResidue (chemistry)Environmental scienceWaste managementEngineeringMaterials scienceChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Bauxite residue is a by-product of alumina refining from bauxite ore according to the Bayer process.During the last two decades, potential use of this by-product in highway engineering projects, which engage big volumes of earthwork, has been investigated through laboratory research and site application.Investigation of bauxite residue properties by different research institutes produced varying chemical composition, values of physical characteristics and strength features.Comparing these research results worldwide, one can easily notice the significant irregularity, attributed, in the first place, to the bauxite ore.However, there are probably, other reasons, as well, which make these test results and, especially, the strength test results, difficult to interpret and, probably, non-comparable.This scientific article presents an overview of chemical analyses and test results on physical and strength properties of bauxite residue worldwide.It also attempts an exploration of the reasons for the disparity of values encountered in the international literature.Moreover, the article presents a recipe to enhance the strength properties of bauxite residue with view to utilising the byproduct for engineering purposes.Two pilot engineering projects, introducing bauxite residue as main construction material, are herewith outlined.The unprecedented success of the pilot projects linked to the excellent performance of the bauxite residue structures pointed out the prospective benefits from the use of this by-product in engineering.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.203
Teacher spread0.187 · 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 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
Published2020
Admission routes1
Has abstractyes

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