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Record W4220694445 · doi:10.1061/9780784484012.037

Thermal Properties of Bio-Cemented Sand

2022· article· en· W4220694445 on OpenAlexaboutno aff
Pinar Gunyol, Mohammad Khosravi, A. J. Phillips, Kathryn Plymesser

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCementation (geology)CalciteCalcium carbonateThermal conductivityChemistryCalciumPrecipitationMaterials scienceCementMineralogyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

A series of thermal experiments was conducted to investigate the effect of ureolysis-driven calcite precipitation technique using the ureolytic bacterium Sporosarcina pasteurii on thermal properties of bio-cemented sand. The sand used in this study was Ottawa F-65 sand and thermal properties of the sand were measured using a TR-3 sensor connected to a portable, battery-operated thermal properties analyzer. The effects of different injection methods of the injected fluids on the efficiency of calcium carbonate precipitation and the uniformity of bio-cementation through the soil specimen were investigated. The amount of urea hydrolyzed, and the amount of calcium precipitated were determined using the modified colorimetric Jung assay and calcium assay, respectively. Calcium concentration along the bio-cemented soil column was determined using acid washing method followed by use of a colorimetric calcium assay. Thermal conductivity measurements were used to evaluate the changes as mineral develops in time.

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 score0.010
Threshold uncertainty score0.021

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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