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Record W2903345023 · doi:10.1371/journal.pone.0207489

Sporosarcina pasteurii can clog and strengthen a porous medium mimic

2018· article· en· W2903345023 on OpenAlexaff
Swayamdipta Bhaduri, Carlo Montemagno

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicroscale chemistryPorous mediumCloggingChemical engineeringPorosityMaterials sciencePrecipitationCompressive strengthScanning electron microscopeNanotechnologyChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

The bacterium Sporosarcina pasteurii can produce significant volumes of solid precipitation in the presence of specific chemical environments. These solid precipitate particles can enter a network of microscale pores and cause long-range clogging. As a result, the medium gains strength and exhibits superior mechanical properties. This concept is also known as Microbiologically Induced Calcite Precipitation (MICP). In this study, we have used sponge blocks as surrogate porous media mimics and analyzed several aspects of MICP. A synergistic approach involving electron microscopy (SEM), computerized X-Ray tomography (μCT), quasi-static compressive load testing and chemical characterization (EDX) has been used to understand several physical and chemical aspects of MICP.

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 categoriesInsufficient payload (model declined to judge)
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 score0.067
Threshold uncertainty score0.995

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)0.0060.001

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 head0.024
GPT teacher head0.204
Teacher spread0.181 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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