The Significance of Breath Sampling Frequency on the Mouth Alcohol Effect
Bibliographic record
Abstract
Retention of mouth alcohol may result in falsely high breath alcohol concentrations and is thus a concern in evidential breath alcohol testing. Frequent breath sampling used in the typical mouth alcohol experiment may underestimate the duration and magnitude of the mouth alcohol effect in comparison to the real-life forensic situation. On each of three separate trials, nineteen female, and eleven male, alcohol-free subjects rinsed their mouths with 20 mL of diluted vodka (20% alcohol v/v) for 20 seconds and then expectorated. On each of the trials, the subjects provided breath samples into an Intoxilyzer® 5000C either every two minutes, every four minutes, or every eight minutes, respectively. The subjects did not talk or open their mouths throughout the experiment except for providing the breath sample. The final breath result displayed, as well as whether or not the instrument detected mouth alcohol, was recorded. As expected, an increase in the frequency of breath sampling significantly decreased the magnitude and duration of the mouth alcohol effect. At eight minutes after rinsing the mouth with alcohol, the mean mouth alcohol concentration (MAC) (± SEM) was 0.072 (± 0.011) g/210 L, 0.057 (± 0.005) g/210 L and 0.041 (± 0.004) g/210 L for breath sampling every eight, four, and two minutes, respectively. At sixteen minutes after rinsing the mouth with the diluted vodka, a positive MAC was found in 47%, 33%, and 13% of the subjects for breath sampling every eight, four, and two minutes, respectively. The rate of detection of mouth alcohol by the Intoxilyzer® 5000C increased from 53% at MACs < 0.050 g/210 L to 100% at MACs > 0.199 g/210 L. Frequent breath sampling leads to a more rapid and greater decrease in the mouth alcohol effect. The ability of correctly detecting mouth alcohol with the Intoxilyzer® 5000C increases with increasing MAC.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".