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Record W2039158740 · doi:10.3354/dao064211

Standardization of a broth microdilution susceptibility testing method to determine minimum inhibitory concentrations of aquatic bacteria

2005· article· en· W2039158740 on OpenAlexaff
RA Miller, RD Walker, J Carson, Mada Coles, Rosie Coyne, Inger Dalsgaard, Charles M. Gieseker, H. M. Hsu, J. J. Mathers, M. Papapetropoulou, B. Denise Petty, C. Teitzel, Renate Reimschuessel

Bibliographic record

VenueDiseases of Aquatic Organisms · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBroth microdilutionLibrary scienceVeterinary medicineBiologyMedicineMinimum inhibitory concentrationMicrobiology

Abstract

fetched live from OpenAlex

A multiple laboratory study was conducted in accordance with the standards established by the Clinical and Laboratory Standards Institute (CLSI), formerly the National Committee for Clinical Laboratory Standards (NCCLS), for the development of quality control (QC) ranges using dilution antimicrobial susceptibility testing methods for bacterial isolates from aquatic animal species. QC ranges were established for Escherichia coli ATCC 25922 and Aeromonas salmonicida subsp. salmonicida ATCC 33658 when testing at 22, 28 and 35 degrees C (E. coli only) for 10 different antimicrobial agents (ampicillin, enrofloxacin, erythromycin, florfenicol, flumequine, gentamicin, ormetoprim/sulfadimethoxine, oxolinic acid, oxytetracycline and trimethoprim/sulfamethoxazole). Minimum inhibitory concentration (MIC) QC ranges were determined using dry- and frozen-form 96-well plates and cation-adjusted Mueller-Hinton broth. These QC ranges were accepted by the CLSI/NCCLS Subcommittee on Veterinary Antimicrobial Susceptibility Testing in January 2004. This broth microdilution testing method represents the first standardized method for determining MICs of bacterial isolates whose preferred growth temperatures are below 35 degrees C. Methods and QC ranges defined in this study will enable aquatic animal disease researchers to reliably compare quantitative susceptibility testing data between laboratories, and will be used to ensure both precision and inter-laboratory harmonization.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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 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

Citations66
Published2005
Admission routes1
Has abstractyes

Explore more

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