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Development of Time-Temperature Probes for Tracking Pathogen Inactivation During Composting

2008· article· en· W1988211318 on OpenAlexaff
Kristine Wichuk, Daryl McCartney

Bibliographic record

VenueCompost Science & Utilization · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of Alberta
FundersU.S. Environmental Protection Agency
KeywordsCompostTracking (education)Particle (ecology)Process (computing)Environmental sciencePhase (matter)PathogenMaterials scienceProcess engineeringWaste managementComputer scienceChemistryBiologyMicrobiologyEngineeringEcology

Abstract

fetched live from OpenAlex

Pathogen inactivation is expected to occur in compost if temperatures over 55°C (131°F) are maintained for at least 3 days, or 15 days in windrows. However, a literature review revealed pathogen survival in a significant number of processes appearing to meet the prescribed time-temperature criteria. It was hypothesized that pathogen survival may be due to the existence of undetected temperature variations within the compost mass. In order to investigate this possibility, it is necessary to monitor the conditions that a random particle of compost would experience as it passes through the composting process. As no adequate methods currently exist to monitor the temperatures of random particles, it was deemed necessary to develop a method to monitor temperature conditions that random particles of compost material encounter during the high-temperature phase of composting. A self-contained, battery powered temperature probe was designed for this purpose, with properties of size and density similar to those of compost particles, in order to allow it to move freely as it undergoes the composting process (in a manner similar to that of a random particle of material). Preliminary tests were done to ensure adequate device operation prior to manufacturing. Though the results were promising, improvements and further testing were recommended to ensure that the probe could withstand the harsh conditions encountered during composting, and to ensure that it would move randomly during compost agitation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.082
GPT teacher head0.276
Teacher spread0.194 · 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 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

Citations12
Published2008
Admission routes1
Has abstractyes

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