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Record W2146737314 · doi:10.3382/japr.2009-00017

Microbiological evaluation of poultry house wall materials and industrial cleaning agents

2009· article· en· W2146737314 on OpenAlexaff
Bruce Rathgeber, K. L. Thompson, Clinton M Ronalds, K. L. Budgell

Bibliographic record

VenueThe Journal of Applied Poultry Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsNova Scotia Department of AgricultureAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDisinfectantPoultry farmingEnvironmental scienceWaste managementBiosecurityFood processingEngineeringFood scienceVeterinary medicineBiologyMedicine

Abstract

fetched live from OpenAlex

Proper cleaning of commercial poultry production units, particularly after the removal of litter, is essential for a successful biosecurity program. The effectiveness of a cleaning regimen depends on numerous variables, such as organic load, building design, and wall and floor materials. The objective of this project was to compare the efficacy of industrial cleaning agents used in food-processing establishments with one commonly used on farm, and to determine if the type of wall material influenced the effectiveness of these products. Iodine, a disinfectant traditionally used in broiler housing, was compared with 2 cleaning agents commonly used in food-processing areas. Each cleaner was applied to 3 types of building material (plywood, metal, and plastic), which had been placed in commercial broiler pens to achieve an organic load. Plywood maintained a higher bacterial load after cleaning than other building material types. Additionally, iodine was not as effective as either the foam or gel cleaner commonly used in food-processing establishments. This information will be useful in developing good sanitization protocols for commercial poultry production.

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)0.008
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 score0.008
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
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 head0.325
GPT teacher head0.434
Teacher spread0.109 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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