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Record W2594800748 · doi:10.1139/cjas-2015-0166

Investigation of prevalence of thermotolerant <i>Campylobacter</i> spp. in livestock feces

2017· article· en· W2594800748 on OpenAlexvenueno aff
Ebrahim Rahimi, Mandana Alipoor-Amroabadi, Faham Khamesipour

Bibliographic record

VenueCanadian Journal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCampylobacterFecesCampylobacter jejuniLivestockBiologyVeterinary medicineCiprofloxacinCampylobacter coliTetracyclineMicrobiologyAntibiotic resistanceAntibioticsBacteriaMedicineEcology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the prevalence of thermotolerant Campylobacter spp. and antimicrobial resistance profiles isolated from in livestock feces in Isfahan, Iran. A total of 400 fecal of livestock samples were collected, and the presence of Campylobacter species was studied by culture and polymerase chain reaction-based assays and antimicrobial susceptibility test. A total of 28 Campylobacter isolates including 22 Campylobacter jejuni and 6 Campylobacter coli were recovered from feces of livestock. The prevalence rates of Campylobacter spp. were observed in this study in sheep (10%), goat (8%), cattle (5.3%), and camel (4%). The highest prevalence of Campylobacter spp. was found in the summer (10%) and the lowest was in winter (4%). Among the isolates from livestock, both C. jejuni and C. coli from fecal samples had the highest frequency of tetracycline (75.1%) and ciprofloxacin (57.1%) resistance. The results of this study showed a high prevalence of Campylobacter spp. in livestock feces in Isfahan, Iran. The presence of Campylobacter in livestock feces can contaminate the environmental and human food chain. Therefore, detection of Campylobacter spp. in livestock-originated samples is important to identify possible sources of infection and to have that a better understanding of the epidemiology of infection virulence genes of isolates is considered essential.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.250
Teacher spread0.204 · 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 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

Citations19
Published2017
Admission routes1
Has abstractyes

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