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Record W2516767907 · doi:10.1061/9780784480137.030

Geotechnical Characterization of Compacted Bauxite Residue for Use in Levees

2016· article· en· W2516767907 on OpenAlexaff
Matthew S. Gore, Robert B. Gilbert, Ian McMillan, Shannon Parks

Bibliographic record

VenueGeo-Chicago 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsBauxiteLeveeGeotechnical engineeringHydraulic conductivityResidue (chemistry)Environmental scienceGeologySoil waterMaterials scienceSoil scienceMetallurgy

Abstract

fetched live from OpenAlex

This paper describes the results from a laboratory test program developed to characterize and evaluate the geotechnical properties of compacted bauxite residue, a byproduct from the production of alumina. Demand for suitable fill materials to construct flood-protection levees, particularly along the U.S. Gulf Coast, prompted this study to determine if bauxite residue could be a feasible solution. The laboratory test program consisted of characterization tests and performance tests including shear strength, hydraulic conductivity, erosion resistance and compressibility. Samples of both untreated bauxite residue and alkalinity-adjusted bauxite residue were tested. The results provide several conclusions. The characterization tests show that bauxite residue behaves like a fine-grained, plastic soil. Oven-drying the material before testing affects its geotechnical behavior and provides impractical results. In a compacted state, bauxite residue has a low hydraulic conductivity that is resistant to internal erosion and suitable for the core of a flood-protection levee. Compacted bauxite residue has a shear strength that is greater than fine-grained soils typically used in levees, potentially reducing the necessary footprint and volume required for a levee constructed with this material. The high alkalinity of untreated bauxite residue makes it necessary for neutralization or encapsulation to minimize its impact on the environment.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.227
Teacher spread0.206 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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