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Record W2917941584 · doi:10.3389/fpubh.2019.00023

Bioaerosols and Transmission, a Diverse and Growing Community of Practice

2019· review· en· W2917941584 on OpenAlexafffund
Samira Mubareka, Nicolas Groulx, Eric Savory, Todd Cutts, Steven Theriault, James A. Scott, Chad J. Roy, Nathalie Turgeon, Elizabeth Bryce, George Astrakianakis, Shelley Kirychuk, Matthieu Girard, Gary Kobinger, Chao Zhang, Caroline Duchaine

Bibliographic record

VenueFrontiers in Public Health · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsInstitut de Recherche et de Développement en AgroenvironnementUniversity of SaskatchewanUniversité LavalVancouver Coastal Health Research InstituteVancouver Coastal HealthInstitut universitaire de cardiologie et de pneumologie de QuébecPublic Health Agency of CanadaUniversity of TorontoWestern UniversityUniversity of British ColumbiaPublic Health OntarioSunnybrook Hospital
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalNational Institutes of Health
KeywordsIndoor bioaerosolTransmission (telecommunications)Environmental healthData scienceMedicineComputer scienceBiologyTelecommunicationsEcology

Abstract

fetched live from OpenAlex

The transmission of infectious microbes via bioaerosols is of significant concern for both human and animal health. However, gaps in our understanding of respiratory pathogen transmission and methodological heterogeneity persist. New developments have enabled progress in this domain, and one of the major turning points has been the recognition that cross-disciplinary collaborations across spheres of human and animal health, microbiology, biophysics, engineering, aerobiology, infection control, public health, occupational health, and industrial hygiene are essential. Collaborative initiatives support advances in topics such as bioaerosol behavior, dispersion models, risk assessment, risk/exposure effects, and mitigation strategies in clinical, experimental, agricultural, and other field settings. There is a need to enhance the knowledge translation for researchers, stakeholders, and private partners to support a growing network of individuals and agencies to achieve common goals to mitigate inter- and intra-species pathogen transmission via bioaerosols.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.077
GPT teacher head0.337
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations38
Published2019
Admission routes2
Has abstractyes

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