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Record W2140791970 · doi:10.7202/014415ar

Utilisation de bactériophages pour contrôler les populations de Aeromonas salmonicida résistantes aux antibiotiques

2007· article· fr· W2140791970 on OpenAlexaffabout
Sandra Imbeault, Serge Parent, Jean-François Blais, Michel Lagacé, Carl Uhland

Bibliographic record

VenueRevue des sciences de l eau · 2007
Typearticle
Languagefr
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité de MontréalMontreal BiodomeInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAeromonas salmonicidaAntibacterial agentHumanitiesMolecular biologyBiologyAntibioticsPhilosophyMicrobiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

La furonculose, causée par la bactérie Aeromonas salmonicida , représente une des principales causes de mortalité chez les salmonidés d’élevage. L’antibiothérapie constitue l’approche la plus largement répandue pour contrer les effets néfastes de cette maladie. Cependant, le développement de bactéries résistantes aux antibiotiques représente un problème de plus en plus préoccupant. La présente recherche a visé à explorer une nouvelle option pour lutter contre la furonculose, soit la possibilité d’utiliser des bactériophages comme moyen de prévention pour contrôler les populations de A. salmonicida . La sensibilité de 19 souches de A. salmonicida , résistantes à aucun, un, deux ou trois antibiotiques, a été évaluée vis‑à‑vis de 12 bactériophages. Les résultats ont montré que les souches de A. salmonicida résistantes aux antibiotiques utilisés dans l’industrie piscicole canadienne sont aussi sensibles à de nombreux bactériophages, tout comme des souches sensibles aux antibiotiques. Il serait donc possible d’envisager un traitement préventif à base de bactériophages pour lutter contre la furonculose chez les salmonidés d’élevage.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.125
GPT teacher head0.370
Teacher spread0.246 · 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 designObservational
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

Citations3
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueRevue des sciences de l eauSame topicBacteriophages and microbial interactionsFrench-language works237,207