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Record W2591588316 · doi:10.1590/0103-8478cr20160837

Adapted Bailenger method improves the rate of Ascaris suum eggs recovery from liquid pig manure compost

2017· article· en· W2591588316 on OpenAlexfundno aff
Mariângela F. de Sá, Ricardo Aymay Gonçalves, Cristiana Marder, Matheus D. Baldissera, Camila Belmonte de Oliveira, Jéssica Caroline Gomes Noll, Filipe Santana da Silva, Sílvia González Monteiro

Bibliographic record

VenueCiência Rural · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMcMaster University
KeywordsAscaris suumCompostManureSawdustAscarisAnimal scienceBiologyChemistryAgronomyHelminthsEcology

Abstract

fetched live from OpenAlex

ABSTRACT: Liquid pig manure (LPM) is widely used as a compost fertilizer for vegetable crops destined for human consumption. However, these wastes may contain parasites eggs, such as the nematode Ascaris suum, that pose serious health risks to humans. We attempted to determine the most appropriate technique for recovering A. suum eggs from LPM compost. Samples were collected from two waste sources during composting, including 23 samples containing LPM, sawdust, and wood shavings, and 14 samples of LPM alone-both in triplicate. Samples were analyzed using several different recovery methods. Recovery of eggs by the modified Bailenger method with adaptations was significantly more effective and recovered 57% more eggs than by the modified Bailenger method alone. Willis-Mollay method, modified Faust method, and the simple sedimentation technique only recovered 4.4%, 13.9%, and 26% of eggs, respectively, compared with the modified Bailenger method with adaptations, indicating that the adjustments made to the Bailenger method were key to improving the recovery of A. suum eggs from compost and LPM.

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 categoriesInsufficient payload (model declined to judge)
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 score0.458
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 studies0.0010.000
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 head0.017
GPT teacher head0.312
Teacher spread0.295 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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