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Record W2400730068 · doi:10.1016/j.rcc.2006.03.001

Filtration of respired gases: theoretical aspects.

2006· review· en· W2400730068 on OpenAlexaff
Ron J. Thiessen

Bibliographic record

VenuePubMed · 2006
Typereview
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsRoyal Columbian Hospital
Fundersnot available
KeywordsRespiratorFiltration (mathematics)Environmental scienceMedicineEnvironmental healthToxicologyBusinessStatisticsMathematicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The filtration of aerosols and the behavior of aerosolized particles are less intuitive and more complex than commonly indicated in the medical literature, but once the basic principles are presented, they are not difficult to understand or apply. Particles with diameters close to the most penetrating particle size are clearly the particles of greatest concern, interest, and value in considering the performance of different filtration devices, and this size has been identified as the standard particle size for testing respirators and breathing system filters. Although almost every level of health care now mandates the N95 (NIOSH rating) as the minimum rating for medical respirators, there is no such mandate regarding minimum efficiencies of breathing system filters. At least in North America, it still falls to each individual purchaser to ensure that these standardized tests are performed, because manufacturers adhere to these standards only on a voluntary basis. Government regulations similar to NIOSH 42 CFR 84 are needed for breathing system filters and should include a rating system such as N95, N99, or N100. For breathing system filters, the BFE and VFE tests are misleading and should be abandoned (or even better, banned) in favor of internationally recognized sodium chloride tests. Until then, manufacturers will be hesitant to abandon their BFE and VFE data, which give the appearance of vastly better performance than does the sodium chloride test.

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 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 score0.993
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.303
Teacher spread0.262 · 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 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

Citations19
Published2006
Admission routes1
Has abstractyes

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