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Record W3210033820 · doi:10.3390/app9061214

Undrained Behavior of Microbially Induced Calcite Precipitated Sand with Polyvinyl Alcohol Fiber

2019· article· en· W3210033820 on OpenAlexaboutno aff
Sun-Gyu Choi, Tung Hoang, Sung-Sik Park

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
FundersNational Research Foundation of Korea
KeywordsCalcitePolyvinyl alcoholMaterials scienceCohesion (chemistry)Hydraulic conductivityComposite materialFiberCementGeotechnical engineeringMineralogyGeologyChemistrySoil science

Abstract

fetched live from OpenAlex

Microbially induced calcite precipitation can cement sand and is an environment-friendly alternative to ordinary Portland cement. In this study, clean Ottawa sand was microbially treated to induce calcite contents (CCs) of 0%, 2%, and 4%. Polyvinyl alcohol fiber was also mixed with the sand at four different contents (0%, 0.2%, 0.4%, and 0.6%) with a constant CC of 4%. A series of undrained triaxial tests was conducted on the treated sands to evaluate the effects of the calcite treatment and fiber inclusion. Their hydraulic conductivity was also determined using a constant head test. As the CC increased from 0% to 4%, the friction angle and cohesion increased from 35.3° to 39.6° and from 0 to 93 kPa, respectively. For specimens with a CC of 4%, as the fiber content increased from 0% to 0.6%, the friction angle and cohesion increased from 39.6° to 42.8° and from 93 to 139 kPa, respectively. The hydraulic conductivity of clean Ottawa sand decreased by a factor of more than 100 as the CC increased from 0% to 4%. The fiber inclusion had less effect on the hydraulic conductivity of the specimen with 4% CC.

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)0.000
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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