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qPCR quantification and genetic characterization of<i>Clostridium perfringens</i>populations in biosolids composted for 2âyears

2009· article· en· W2031594853 on OpenAlexaff
E. Karpowicz, Amy Novinscak, Feli× Bärlocher, Martin Filion

Bibliographic record

VenueJournal of Applied Microbiology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversité de MonctonMount Allison University
Fundersnot available
KeywordsClostridium perfringensBiosolidsCompostTaqManBiologyMicrobiologyAflatoxinIndicator organismGenotypeVeterinary medicineBiotechnologyFood scienceEnvironmental sciencePolymerase chain reactionGeneBacteriaAgronomyGeneticsMedicineEnvironmental engineering

Abstract

fetched live from OpenAlex

AIM: The ability of Clostridium perfringens to survive for a long time in the environment makes it a suitable indicator of faecal pollution, but its use as a routine indicator organism in biosolids and composted biosolids has not yet been adopted. This study was performed to improve our understanding of C. perfringens persistence in composted biosolids by monitoring its presence and studying its genetic diversity. METHODS AND RESULTS: A culture-independent TaqMan qPCR assay targeting the cpn60 gene was adapted to enumerate C. perfringens in composted biosolid samples varying in age from 1 to 24 months. The pathogen was detected in all compost samples under study, but no correlation between composting time and number of cpn60 copies was observed. Rep-PCR detected 14 different C. perfringens genotypes, all belonging to toxinotype A, which is the most common biotype found in human and animal gastrointestinal tracts. CONCLUSIONS: Composting did not significantly decrease the number of C. perfringens cells. High genetic diversity of C. perfringens isolates present in composted biosolids is reported for the first time. SIGNIFICANCE AND IMPACT OF STUDY: This study evaluated tools for surveillance of composting processes, source tracking and risk assessment of composted biosolids.

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 categoriesnone
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 score0.365
Threshold uncertainty score0.158

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)0.0000.000

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 head0.035
GPT teacher head0.256
Teacher spread0.221 · 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 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

Citations23
Published2009
Admission routes1
Has abstractyes

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