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Record W3165257942 · doi:10.3390/su13115836

Interaction between Biofilm Formation, Surface Material and Cleanability Considering Different Materials Used in Pig Facilities—An Overview

2021· article· en· W3165257942 on OpenAlexafffund
Erika Yukari Nakanishi, Joahnn H. Palacios, Stéphane Godbout, Sébastien Fournel

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversité LavalInstitut de Recherche et de Développement en Agroenvironnement
FundersMitacs
KeywordsBiofilmContaminationPersistence (discontinuity)Environmental scienceWaste managementBacteriaEnvironmental engineeringPulp and paper industryEcologyBiologyEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Sometimes the contamination in pig facilities can persist even after the washing and disinfection procedure. Some factors could influence this persistence, such as bacteria type, biofilm formation, material type and washing parameters. Therefore, this review summarizes how the type of surface can influence bacteria colonization and how the washing procedure can impact sanitary aspects, considering the different materials used in pig facilities. Studies have shown that biofilm formation on the surface of different materials is a complex system influenced by environmental conditions and the characteristics of each material’s surface and group of bacteria. These parameters, along with the washing parameters, are the main factors having an impact on the removal or persistence of biofilm in pig facilities even after the cleaning and disinfection processes. Some options are available for proper removal of biofilms, such as chemical treatments (i.e., detergent application), the use of hot water (which is indicated for some materials) and a longer washing 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 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)0.001
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 score0.053
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.029
GPT teacher head0.291
Teacher spread0.262 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

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