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Record W4311582290 · doi:10.1038/s41597-022-01860-y

Particle filtration efficiency measured using sodium chloride and polystyrene latex sphere test methods

2022· article· en· W4311582290 on OpenAlexafffund
Timothy A. Sipkens, Joel C. Corbin, Andrew Oldershaw, Gregory J. Smallwood

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsNational Research Council Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsPolystyreneDispersityFiltration (mathematics)Materials scienceSodiumEquivalence (formal languages)ChlorideParticle sizeParticle (ecology)MathematicsChemistryPolymer chemistryStatisticsComposite materialMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Standards governing face masks differ in the test methods used to determine sub-micron particle filtration efficiency (PFE), such that the meaning of PFE is not universal. Unifying the meaning of PFE requires data using these different test methods to drive improvements in standards. This simple data set provides the equivalence between two major test methods used to assess PFE: (1) a test method using a neutralized, polydisperse sodium chloride (NaCl) and (2) a test method using an unneutralized, "monodisperse" polystyrene latex sphere (PSL) aerosols. Measurements are made on over 5800 real-world medical masks, leading to the establishment of a relationship between these two kinds of PFE for these 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)0.003
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.099
GPT teacher head0.365
Teacher spread0.266 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

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