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Record W4388498008 · doi:10.3390/v15112224

ECOPHAGE: Combating Antimicrobial Resistance Using Bacteriophages for Eco-Sustainable Agriculture and Food Systems

2023· article· en· W4388498008 on OpenAlexaff
Pilar García, Rafael Tabla, Hany Anany, Roberto Bastías, Lone Brøndsted, Susana Casado, Pablo Cifuentes, Thomas G. Denes, Mohammad Aminul Islam, Rob Lavigne, Andrea I. Moreno‐Switt, Natsuko Nakayama, Cristina Muñoz Madero, Alexander Sulakvelidze, Antonet M. Svircev, Jeroen Wagemans, Elena G. Biosca, Dácil Rivera

Bibliographic record

VenueViruses · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersOrganisation de Coopération et de Développement ÉconomiquesUniversitat de València
KeywordsAgricultureBiotechnologyBusinessAgency (philosophy)Sustainable agricultureAnimal husbandryFood industryBiologyFood science

Abstract

fetched live from OpenAlex

The focus of this meeting was to discuss the suitability of using bacteriophages as alternative antimicrobials in the agrifood sector. Following a One Health approach, the workshop explored the possibilities of implementing phage application strategies in the agriculture, animal husbandry, aquaculture, and food production sectors. Therefore, the meeting had gathered phage researchers, representatives of the agrifood industry, and policymakers to debate the advantages and potential shortcomings of using bacteriophages as alternatives to traditional antimicrobials and chemical pesticides. Industry delegates showed the latest objectives and demands from consumers. Representatives of regulatory agencies (European Medicines Agency (EMA) and Spanish Agency of Medicines and Health Products (AEMPS)) presented an update of new regulatory aspects that will impact and support the approval and implementation of phage application strategies across the different sectors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.592

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.258
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.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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