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Record W2466518844 · doi:10.7843/kgs.2016.32.5.5

Engineering Characteristics of Cemented Sand with Microorganism Using Eggshell as Calcium Source

2016· article· en· W2466518844 on OpenAlexaboutno aff
Sun-Gyu Choi, Sung-Sik Park

Bibliographic record

VenueJournal of the Korean Geotechnical Society · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringMicroorganismEggshellCalciumGeologyMaterials scienceMetallurgyBacteria

Abstract

fetched live from OpenAlex

A calcium source is necessary for calcite precipitation within soil particles by microbial decomposition of urea and a calcium chloride is usually used. The harmful environmental impact of calcium chloride on road, ground and plants is severe. In this study, an eggshell with vinegar is investigated for an environmental-friendly calcium source. Urea-decomposing microorganism and eggshell or calcium chloride as a calcium source are mixed with Ottawa sand to precipitate calcite. Then, the cemented sand with calcite is tested for calcite precipitation, permeability and unconfined compressive strength. A specimen is prepared by loose Ottawa sand in a size of 5 cm in diameter and 10 cm in height. A urea solution with Sporosarcina pasteurii and two different calcium sources is injected into the specimen once a day for 30 days. Calcite precipitated at average of 7.2% on the specimen with eggshell as a calcium source, which was 1.2 times more than that with calcium chloride. The permeability of a specimen with eggshell was at average of 3.82E-5 cm/s, which was 7.7 times lower than that with calcium chloride. Unconfined compressive strength of a specimen with eggshell was at average of 387 kPa, which was 1.2 times higher than that with calcium chloride. As more calcite precipitated, the strength increased while the permeability decreased, regardless of calcium sources.

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 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 score0.052
Threshold uncertainty score0.664

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)0.0010.000

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 teacher head, 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of the Korean Geotechnical SocietySame topicMicrobial Applications in Construction MaterialsFrench-language works237,207