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Record W4226025306 · doi:10.1590/1678-5150-pvb-6990

Ammonia gas for bacterial control in poultry litter

2022· article· en· W4226025306 on OpenAlexaff
Richard F. Muniz, Willian Rafael de Oliveira, Rhaquel S. Pereira, Cristiani V. Pasqualotto, Luciana Ruschel dos Santos, Laura Beatriz Rodrigues, Bruno S. Mendonça, Luciane Daroit, Fernando Pilotto

Bibliographic record

VenuePesquisa Veterinária Brasileira · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsInnovation, Science and Economic Development Canada
FundersUniversidade de Passo Fundo
KeywordsLitterPoultry litterPoultry farmingMicroorganismBacteriaPathogenic bacteriaEnvironmental scienceFood scienceBiologyVeterinary medicineMedicineNutrientEcology

Abstract

fetched live from OpenAlex

ABSTRACT: The current techniques used in the disinfection of reused poultry litter, such as lime addition, windrowing and plastic cover on the surface, do not guarantee the elimination of pathogenic microorganisms, causing damage to the environment and animal health. Gram negative bacteria, i.e., Salmonella and Escherichia coli, can be transmitted from one batch to another through reused litter, causing health damage to broilers and humans that consume food contaminated by these agents. Our study assessed the effectiveness of the methods plastic cover on the surface (PCS) and plastic cover on the surface with ammonia gas injection (PCSAI) in the control of Gram negative bacteria. The results obtained, both in laboratory conditions (Experiment 1) and in the field (Experiment 2), demonstrate that the method PCSAI with 0.22% ammonia gas had a significant reduction (P<0.05) of Gram negative bacteria in the period of 48 hours This new methodology for disinfecting poultry litter will allow its reuse in a practical and safe way, improving the preservation of the environment, of the health of broilers and consumers of poultry products.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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