Collection of Δ<sup>9</sup>-tetrahydrocannabinol from breath by liquid secondary adsorption analyzed with mass spectrometry: a technical note
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We introduce a novel method for efficient collection of analytes of low volatility from human breath, liquid secondary adsorption (LSA), and the application of this method to drug detection with mass spectrometry. Cannabis legalization has occurred in many jurisdictions, creating a need for a simple method for detection of recency of use. Most existing breath sampling methods rely on a time consuming and complex process of adsorption of the analyte of interest, and still often result in low collection efficiencies. The pilot study shows the capability of a breath capture technique and mass spectrometry add on analysis device (Cannabix Breath Analysis System) to easily collect breath samples in the field and rapidly analyze them without complex sample preparation. The study also shows correlation between the breath data collected with this method and blood Δ 9 -tetrahydrocannabinol (THC) levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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