A Test Bench for Replicating Human Breathing: Evaluating Thermal Effects of N95 Filtering Facepiece Respirator Leaks – Preliminary Findings
Bibliographic record
Abstract
Following the COVID-19 pandemic, it seemed important to develop alternative methods for verifying the fit of a filtering f acepiece respirator (FFR). I nfrared technology, promising in the assessment of the fit of a F FR, still requires a thorough study of the thermal effect of a N95 FFR leak. This pilot study aims to design a test bench capable of replicating human breathing as faithfully as possible, focusing on three aspects: breathing airflow, breathing temperature, a nd particle generation. The humidity aspect is not replicated in this study. To achieve this, the ASL 5000® Breathing Simulator, a heating chamber, and a HEPA filter were employed. These three aspects were validated through various tests on the test bench. The primary objective of this test bench is to assess the thermal impact of different leaks from an N95 FFR on two different test devices designed for this study: a flat model a nd a Static Advanced Headform (StAH). The thermal impact of the leaks was captured using an infrared (IR) camera. Additionally, a particle counter, the PortaCount Instrument, was integrated into the test bench to quantify N95 FFR leaks and was used as a reference. In the near future, this test bench will enable the development of methods to locate and quantify N95 FFR leaks, which will subsequently be applied to human subjects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".