Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The first and most crucial step in breath research is adequate sampling, which plays a pivotal role in quality assurance of breath datasets. In particular, the emissions or uptake of volatile organic compounds (VOCs) by sampling interface materials present a risk of disrupting breath gas samples. This study investigated emissions and uptake by three interface components, namely a silicon facemask, a reusable 3D-printed mouthpiece adapter, and a pulmonary function test filter compatible with the commercial Respiration Collector for<i>In-Vitro</i>Analysis (ReCIVA) breath sampling device. Emissions were examined before and after (hydro-)thermal treatment of the components, and uptake was assessed by exposing each material to 12 representative breath VOCs comprising alcohols, aldehydes, ketones, carboxylic acids, terpenes, sulphurous and nitrogenous compounds at different target concentration ranges (∼10 ppb<sub>V</sub>and ∼100 ppb<sub>V</sub>). Chemical analyses of VOCs were performed using proton transfer reaction-time-of-flight-mass spectrometry (PTR-TOFMS) with supporting analyses via thermal desorption comprehensive two-dimensional gas chromatography-TOFMS (TD-GC×GC-TOFMS). The filter exhibited the lowest overall emissions compared to the mask or adapter, which both had equivalently high emissions (albeit for different compounds). Treatment of the materials reduced the total VOC emissions by 62% in the mask, 89% in the filter and 99% in the adapter. Uptakes of compounds were lowest for the adapter and most pronounced in the mask. In particular, 1-butanol, acetone, 2-butanone, 1,8-cineole and dimethyl sulphide showed negligible uptake across all materials, whereas ethanol, nonanal, acetic acid, butanoic acid, limonene and indole exhibited marked losses. Knowledge of emissions and/or uptake by sampling components is key to reducing the likelihood of erroneous data interpretation, ultimately expediting progress in the field of breath test development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 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) | 0.000 | 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 it