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Pathogen Characterization of Fresh and Stored Mesophilic Anaerobically Digested Biosolids

2017· article· en· W2905263479 on OpenAlexaffabout
Cecily A. Flemming, Albert Simhon, Joseph Odumeru

Bibliographic record

VenueWater Environment Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversity of GuelphMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsBiosolidsCryptosporidium parvumMicrobiologySalmonellaCryptosporidiumListeria monocytogenesBiologyGiardia lambliaIndicator bacteriaClostridium perfringensGiardiaFood scienceYersinia enterocoliticaBacteriaFecal coliformFecesEnvironmental scienceEnvironmental engineeringWater qualityEcology

Abstract

fetched live from OpenAlex

Culturable bacterial pathogens (Campylobacter, Salmonella, Listeria, Yersinia) and indicators (E. coli, enterococci, Clostridium perfringens) were quantified at six water resource recovery facilities that land apply anaerobically digested biosolids in Ontario, Canada. Cryptosporidium parvum and Giardia lamblia were also quantified by polymerase chain reaction (PCR). Salmonella and Listeria were frequently detected in sludge and liquid biosolids (70-100% of samples) but less often in fresh dewatered cake biosolids (50-60%); with low levels in fresh cake (<100 cells/g dw). Yersinia were in 20 to 30% of samples, typically at very low levels (<10 cell/g dw). Giardia and Cryptosporidium were detected in 80 and 20% of cake biosolids at geometric means of 270 cysts/g dw and 70 oocysts/g dw, respectively. E. coli reduction was typically >2-log10 while pathogen reduction was variable. "Sudden increase" of pathogens was not observed, however, Salmonella and E. coli showed regrowth (at 1 to 3 orders of magnitude) after 2- to 3-day storage at 30 °C.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.076
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.054
GPT teacher head0.311
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2017
Admission routes2
Has abstractyes

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