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To Be or Not to Be Mesophilic, That Is the Question for Aeromonas salmonicida

2022· article· en· W4214767615 on OpenAlexaff
Antony T. Vincent, Steve J. Charette

Bibliographic record

VenueMicroorganisms · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
Fundersnot available
KeywordsPsychrophileAeromonas salmonicidaMesophileBiologyEcological nicheAeromonasBacteriaEcologyMicrobiologyFish <Actinopterygii>ZoologyFisheryGeneticsHabitat

Abstract

fetched live from OpenAlex

The bacterium Aeromonas salmonicida has long been known to be one of the most feared pathogens in fish farming. However, the more we discover about this bacterial species, the more we question whether it is really exclusively an aquatic pathogen. In recent years, it has become obvious that this bacterial species includes a myriad of strains with various lifestyle and ecological niches, including the well-known strict psychrophiles, the first bacteria known of the species, and the newly described mesophilic strains. The mesophiles are able to grow at low temperatures, but even better at temperatures of approximately 37 °C, which strict psychrophiles cannot do. In this perspective article, we address some aspects surrounding this dual lifestyle in A. salmonicida, including the impact of mobile genetic elements, and how future research around this bacterial species may focus on the psychrophilic/mesophilic dichotomy, which makes A. salmonicida an increasingly interesting and relevant model for the study of speciation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.006
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.279
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2022
Admission routes1
Has abstractyes

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