Topical review on monitoring tetrahydrocannabinol in breath
Bibliographic record
Abstract
Legalization of cannabis for recreational use has compelled governments to seek new tools to accurately monitor Δ9-tetrahydrocannabinol (Δ9-THC) and understand its effect on impairment. Various methods have been employed to measure Δ9-THC, and its respective metabolites, in different biological matrices. Recently, breath analysis has gained interest as a non-invasive method for the detection of chemicals that are either produced as part of biological processes or are absorbed from the environment. Existing breath analyzers function by analyzing previously collected samples or by direct real-time analysis. Portable hand-held devices are of particular interest for law enforcement and personal use. This paper reviews and compares both commercially available and prototype devices that proclaim Δ9-THC detection in exhaled breath using methods such as Field Asymmetric Ion Mobility Spectrometry, Semiconductor-Enriched Single-Walled Carbon Nanotube chemiresistors, Liquid Chromatography Tandem-mass Spectrometry, microfluidic-based artificial olfaction, and optical-based gas sensing.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".