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Record W2795648195 · doi:10.5539/jfr.v7n3p64

Assessment of Pathogen Inactivation under Sub-composting Temperature in Lab-scale Compost Piles

2018· article· en· W2795648195 on OpenAlexvenueno aff
Venkata Vaddella, Pramod Pandey, Wenlong Cao, Sagor Biswas, Collen Chiu, Yawen Zheng, Tong Wu, Nada Ghanem, Fatïh Büyüksönmez

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCompostRaw materialPathogenEscherichia coliEnvironmental scienceChemistryBiologyMicrobiologyAgronomyEcologyBiochemistry

Abstract

fetched live from OpenAlex

This study was conducted to assess the temperature profile and corresponding pathogen inactivation in lab-scale compost piles. The variation in temperature at different locations of piles and E. coli concentrations was evaluated. The experiment design included plastic containers of different height filled with organic feedstock. Cotton balls soaked with pathogens (E. coli and E. coli O157:H7) were placed inside the feedstocks at various depths. Subsequently, change in pathogen concentrations, feedstock characteristics, and temperature was monitored over time. Observations showed fluctuation in temperature of piles. The peak temperature (> 50 °C) was reached after two weeks of expertiment. The concentrations of E.coli and E. coli O157: H7 at different depths varied among piles during the 35 days of experiments. The reductions in E. coli concentrations ranged 1- 4 orders of magnitude. In certain piles, reduction in E. coli concentrations was followed by increased in E. coli levels indicating the possibility of perturbation of bacteria in the feedstock potentially at low temperature. We anticipate these preliminary results will provide additional insights on pathogen inactivation in compost system. The approach used here can be implemented at field-scale compost piles for assessing pathogen inactivation during compost process under field conditions.

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)0.003
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 score0.602
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.378
Teacher spread0.292 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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