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Record W2088689140 · doi:10.1139/w06-103

Sampling bias created by ampicillin in isolation media for<i>Aeromonas</i>

2007· article· en· W2088689140 on OpenAlexvenueno aff
Jennifer R. Huddleston, John C. Zak, Randall M. Jeter

Bibliographic record

VenueCanadian Journal of Microbiology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsnot available
FundersTexas Tech University
KeywordsAmpicillinAeromonasMicrobiologyIsolation (microbiology)BiologyPopulationAntibioticsBacteriaMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Members of the bacterial genus Aeromonas are widely isolated from aquatic environments and studied in part for their ability to act as opportunistic pathogens in a variety of animals. All aeromonads, with the exception of Aeromonas trota, are generally thought to be resistant to ampicillin, so the antibiotic is frequently added to isolation medium as a selective agent. In this study, 282 aeromonads from environmental sources were isolated on a medium without ampicillin and their resistance to ampicillin determined. Of the 104 of these isolates that were judged to be independent (nonredundant), 18 (17.3%) were susceptible to ampicillin. A chi-square analysis was performed to determine the impact of ampicillin use on enumerating Aeromonas species from environmental samples. Our results indicate that, when ampicillin is used as a selective agent, a significant portion of the aeromonad population in at least some environments can be omitted from isolation.

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)0.010
metaresearch head score (Gemma)0.026
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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

Citations31
Published2007
Admission routes1
Has abstractyes

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