Creation of a standardized geometry of the human nasal cavity
Bibliographic record
Abstract
A novel, standardized geometry of the human nasal cavity was created by aligning and processing 30 sets of computed tomography (CT) scans of nasal airways of healthy subjects. Digital three-dimensional (3-D) geometries of the 60 single human nasal cavities (30 right and 30 mirrored left cavities) were generated from the CT scans and measurements of physical parameters of each single nasal cavity were performed. A methodology was developed to scale, orient, and align the nasal geometries, after which 2-D digital coronal cross-sectional slices were generated. With the use of an innovative image processing algorithm, median cross-sectional geometries were created to match median physical parameters while retaining the unique geometric features of the human nasal cavity. From these idealized 2-D images, an original 3-D standardized median human nasal cavity was created. This new standardized geometry was compared against the original geometries of all subjects as well as limited existing data from the literature. The new model has potential for use as a geometric standard in future experimental and numerical studies of deposition of inhaled aerosols, as well as for use as a reference during diagnosis of unhealthy patients. The specific procedure developed could also be applied to build standard nasal geometries for different identifiable groups within the larger population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".