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Bacteriophages: Combating Antimicrobial Resistance in Food-Borne Bacteria Prevalent in Agriculture

2021· review· en· W4200073026 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroorganisms · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsCampylobacterAntimicrobialLivestockFood chainCampylobacter jejuniAgriculturePhage therapyBiologyBiotechnologySalmonellaMicrobiologyBacteriaBacteriophageEscherichia coliEcology

Abstract

fetched live from OpenAlex

Through recent decades, the subtherapeutic use of antibiotics within agriculture has led to the widespread development of antimicrobial resistance. This problem not only impacts the productivity and sustainability of current agriculture but also has the potential to transfer antimicrobial resistance to human pathogens via the food supply chain. An increasingly popular alternative to antibiotics is bacteriophages to control bacterial diseases. Their unique bactericidal properties make them an ideal alternative to antibiotics, as many countries begin to restrict the usage of antibiotics in agriculture. This review analyses recent evidence from within the past decade on the efficacy of phage therapy on common foodborne pathogens, namely, Escherica coli, Staphylococcus aureus, Salmonella spp., and Campylobacter jejuni. This paper highlights the benefits and challenges of phage therapy and reveals the potential for phages to control bacterial populations both in food processing and livestock and the possibility for phages to replace subtherapeutic usage of antibiotics in the agriculture sector.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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