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Stiffness and Dilatancy Improvements in Uncemented Sands Treated through MICP

2015· article· en· W1846465020 on OpenAlexaboutno aff
Sean T. O’Donnell, Edward Kavazanjian

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCementation (geology)DilatantGeotechnical engineeringLoessCarbonateStiffnessSoil stabilizationPenetration (warfare)Soil waterDenitrifying bacteriaGeologyMaterials scienceSoil scienceDenitrificationComposite materialMetallurgyChemistryCementGeomorphologyEngineering

Abstract

fetched live from OpenAlex

Laboratory testing shows that microbially induced carbonate precipitation (MICP) through microbial denitrification can improve the mechanical properties of a sand without inducing significant interparticle cementation. Consolidated isotropically undrained triaxial compression testing of Ottawa 20–30 sand treated with denitrifying microorganisms shows that, even at low carbonate contents and with no observed cementation, soil treated through MICP exhibits significantly improved stiffness and dilatant behavior. These improvements are also evident when the treated soil is dried, reconstituted, and retested, indicating that the stiffness and dilatant properties of the soil can be improved by MICP in the absence of interparticle cementation, particularly at low strains. However, these improvements may be reduced or eliminated when the soil is reconstituted and tested multiple times. These results indicate that small amounts of MICP can induce significant improvement in treated soils, potentially leading to savings in time and money if this technology is applied in the field.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations63
Published2015
Admission routes1
Has abstractyes

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