Automated dander dispersal in a cat Naturalistic Exposure Chamber
Bibliographic record
Abstract
Allergen exposure chambers (AEC) provide controlled allergen exposure to allergic subjects for the clinical study of asthma and allergy. They should ideally mimic natural allergen exposure, and provide better control of allergen exposure than possible in field studies. For AEC exposure to cat allergens, typically, either liquid allergen extract is nebulized, or natural cat hair and dander are aerosolized by shaking cat bedding. While bedding shaking is more naturalistic than liquid extract exposure, it results in high variability of allergen levels. We have developed an automated method of natural dander dispersal that uses robotic vacuum cleaners with filters removed and modified for variable suction. The system was validated in two rooms (14.4 m3 and 36.7 m3) where two cats reside. The vacuums aerosolize aspirated dander that has naturally accumulated on the floor. Dispersion was characterized by measured airborne allergen (Fel d 1) and particle sizes and concentrations in time and space during 1 and 2-hour tests. At optimized parameters, Fel d 1 was found to be stable in time (2 hours), and homogenous throughout the rooms. Average Fel d 1 was 55 (±9 SD) ng/m3 in the smaller room and 79 (±30 SD) ng/m3 in the larger room, which are comparable to exposure in homes with cats. This novel method of dander dispersal provides controlled, safe exposure to cat allergens in a clinical setting, while maintaining the naturalistic advantages of a field exposure.
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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 | 0.001 | 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".