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Record W2953441995 · doi:10.1080/09603123.2019.1634799

Settled dust assessment in clinical environment: useful for the evaluation of a wider bioburden spectrum

2019· article· en· W2953441995 on OpenAlexaff
Carla Viegas, Beatriz Almeida, Ana Monteiro, Inês Paciência, João Cavaleiro Rufo, Elisabete Carolino, Anita Quintal Gomes, Magdalena Twarużek, Robert Kosicki, Geneviève Marchand, Liliana Aranha Caetano, Susana Viegas

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
FundersFundação para a Ciência e a Tecnologia
KeywordsBioburdenEnvironmental scienceRhizopusSampling (signal processing)Veterinary medicineBiologyMicrobiologyMedicineFood scienceEngineeringFilter (signal processing)

Abstract

fetched live from OpenAlex

The collection and analysis of settled dust samples from indoor environments has become one of several environmental sampling methods used to assess bioburden indoors. The aim of the study was to characterize the bioburden in vacuumed settled dust from 10 Primary Health Care Centers by culture based and molecular methods. Results for bacterial load ranged from 1 to 12 CFU.g−1 of dust and Gram-negative bacteria ranged between 1 to 344 CFU.g−1 of dust. Fungal load ranged from 0 CFU.g−1 of dust to uncountable. Aspergillus section Fumigati was detected in 4 sampling sites where culture base-methods could not identify this section. Mucorales (Rhizopus sp.) was observed on 1 mg/L voriconazole. Three out of 10 settled dust samples were contaminated by mycotoxins. Settled dust sampling coupled with air sampling in a routine way might provide useful information about bioburden exposure.

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)0.020
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.182
GPT teacher head0.494
Teacher spread0.312 · 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 designObservational
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

Citations25
Published2019
Admission routes1
Has abstractyes

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