Interlaboratory comparison of particle filtration efficiency testing equipment
Bibliographic record
Abstract
Particle filtration efficiency (PFE) is a critical property of face masks, with the most common test methods using sodium chloride as a challenge aerosol. In the absence of bottom-up uncertainty budgets for PFE, interlaboratory comparisons provide an alternative route to robustly quantify the precision and bias of the method. This work presents the results of several interlaboratory comparisons of particle filtration efficiency performed across a network of laboratories. Using log-penetration as a surrogate for PFE, it is shown that expanded reproducibility intervals were consistent across most samples, at around 26% of the nominal value of log-penetration. Between-laboratory contributions to this reproducibility were significant, nearly doubling the lab-reported uncertainties in most instances and emphasizing the need for ongoing interlaboratory studies to be performed for particle filtration. More work is required to identify the causes of these between-laboratory differences, requiring dedicated testing. Alongside uncertainty quantification, testing materials across a range of variables (such as the number of layers, amount of charge on the material, and basis weight) affirm that constant quality is a good approximation when layering or changing the basis weight on an otherwise identical material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".