Deposition of micrometer-sized aerosol particles in neonatal nasal airway replicas
Bibliographic record
Abstract
The nasal aerosol filtration properties of infants 0–3 months old have been quantified through in vitro measurements. Computed tomography (CT) scan data was obtained of seven individuals with ages of 5–79 days. Nasal airway replicas based on these images were manufactured using rapid prototyping. Deposition in the replicas was measured using an electrical low pressure impactor (ELPI) to measure the concentration of aerosol particles in the inertial regime. Comparing the difference in concentration when sampling through the model versus sampling through a blank line gave the deposition fraction. Deposition was measured for particles with aerodynamic diameters between 0.53 and 5.54 μm. Nonlinear least squares curve fitting was performed to collapse intersubject variability and represent the data with a single curve. To achieve satisfactory intersubject variability collapse, a non-dimensional pressure drop, the Euler number (Eu), was required in addition to the Reynolds number (Re) and the particle Stokes number (Stk) where the dimensionless parameters are evaluated with a length scale, D, defined as the airway volume divided by the airway surface area. The equation describing the deposition fraction, η, is η = 1- (14590 / (14590 + Stk1.2201Re1.7742Eu1.5772))0.3687. An analysis of the expected intersubject variability in in vivo deposition was also performed, yielding a method for predicting variance in neonatal nasal airway deposition.Copyright © 2018 American Association for Aerosol Research
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".