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Record W4200082597 · doi:10.3389/fmicb.2021.766242

Environmental Biofilms as Reservoirs for Antimicrobial Resistance

2021· article· en· W4200082597 on OpenAlexafffund

Bibliographic record

VenueFrontiers in Microbiology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAntibioticsAntibiotic resistanceBacteriaBiofilmAntimicrobialResistance (ecology)

Abstract

fetched live from OpenAlex

Characterizing the response of microbial communities to a range of antibiotic concentrations is one of the strategies used to understand the impact of antibiotic resistance. Many studies have described the occurrence and prevalence of antibiotic resistance in microbial communities from reservoirs such as hospitals, sewage, and farm feedlots, where bacteria are often exposed to high and/or constant concentrations of antibiotics. Outside of these sources, antibiotics generally occur at lower, sub-minimum inhibitory concentrations (sub-MICs). The constant exposure to low concentrations of antibiotics may serve as a chemical "cue" that drives development of antibiotic resistance. Low concentrations of antibiotics have not yet been broadly described in reservoirs outside of the aforementioned environments, nor is the transfer and dissemination of antibiotic resistant bacteria and genes within natural microbial communities fully understood. This review will thus focus on low antibiotic-concentration environmental reservoirs and mechanisms that are important in the dissemination of antibiotic resistance to help identify key knowledge gaps concerning the environmental resistome.

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 score0.259
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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

Citations82
Published2021
Admission routes2
Has abstractyes

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