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Evaluation of Repetitive Extragenic Palindromic‐PCR for Discrimination of Fecal <i>Escherichia coli</i> from Humans, and Different Domestic‐ and Wild‐Animals

2007· article· en· W1996596613 on OpenAlexaff
Bidyut R. Mohapatra, Klaas Broersma, Rick Nordin, Asit Mazumder

Bibliographic record

VenueMicrobiology and Immunology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsKamloops Art GalleryAgriculture and Agri-Food CanadaUniversity of Victoria
Fundersnot available
KeywordsBiologyFecesPolymerase chain reactionEscherichia coliDNA profilingPrimer (cosmetics)Veterinary medicineMicrobiologyZoologyGeneticsDNAGene

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the potential of repetitive extragenic palindromic anchored polymerase chain reaction (rep-PCR) in differentiating fecal Escherichia coli isolates of human, domestic- and wild-animal origin that might be used as a molecular tool to identify the possible source(s) of fecal pollution of source water. A total of 625 fecal E. coli isolates of human, 3 domestic- (cow, dog and horse) and 7 wild-animal (black bear, coyote, elk, marmot, mule deer, raccoon and wolf) species were characterized by rep-PCR DNA fingerprinting technique coupled with BOX A1R primer and discriminant analysis. Discriminant analysis of rep-PCR DNA fingerprints of fecal E. coli isolates from 11 host sources revealed an average rate of correct classification of 79.89%, and 84.6%, 83.8%, 83.3%, 82.5%, 81.6%, 80.8%, 79.8%, 79.3%, 77.4%, 73.2% and 63.6% of elk, human, marmot, mule deer, cow, coyote, raccoon, horse, dog, wolf and black bear fecal E. coli isolates were assigned to the correct host source. These results suggest that rep-PCR DNA fingerprinting procedures can be used as a source tracking tool for detection of human- as well as animal-derived fecal contamination of water.

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 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 score0.815
Threshold uncertainty score0.340

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 studies0.0000.001
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 head0.023
GPT teacher head0.276
Teacher spread0.253 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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