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Record W2058686145 · doi:10.1093/ps/86.4.610

Influence of Temperature on Survival and Conjugative Transfer of Multiple Antibiotic-Resistant Plasmids in Chicken Manure and Compost Microcosms

2007· article· en· W2058686145 on OpenAlexafffund
Jiewen Guan, A. Wasty, C. Grenier, M. Chan

Bibliographic record

VenuePoultry Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsMicrocosmCompostPlasmidManureChicken manureMicrobiologyEscherichia coliIncubationBiologyBacteriaAntibioticsFood scienceChemistryAnimal scienceGeneAgronomyEcologyGenetics

Abstract

fetched live from OpenAlex

The aim of this study was to determine if mobile plasmids carrying antibiotic-resistant genes could survive and be transferred in chicken manure maintained under conditions similar to those found in commercial cage layer operations and during composting. Escherichia coli J5 harboring a self-transmissible plasmid (RP4) and E. coli C600 harboring a mobile plasmid (pIE723) were used as plasmid donors; E. coli CV601 was used as a plasmid recipient. At 23 degrees C both plasmids were transferred to E. coli CV601 in chicken manure and in compost microcosms that consisted of a mixture of chicken manure and peat. The transfer frequencies ranged from 8.1 x 10(-5) to 2.4 x 10(-3) per donor cell in manure and from 2.4 x 10(-5) to 5.5 x 10(-4) per donor cell in compost microcosms. After 45 d of incubation at 23 degrees C, RP4, but not pIE723, was recovered by an exogenous isolation method although their E. coli hosts were not cultured from the microcosms. However, when the temperatures of the compost microcosms were elevated to 50 degrees C or above, neither the plasmids nor their E. coli hosts could be detected. The results suggested that composting of chicken manure at high temperatures could help prevent the spread of antibiotic-resistant genes via plasmids in the environment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations51
Published2007
Admission routes2
Has abstractyes

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