Concentration units used to report blood- and breath-alcohol concentration for legal purposes are important to consider when blood-breath ratios of alcohol are calculated and compared between countries: re-evaluation of a German study with Alcotest 9510 DE evidential instrument
Bibliographic record
Abstract
This article re-evaluates the results of a controlled drinking study done in Germany to determine inter-subject variation in the blood-to-breath ratio (BBR) of alcohol. Statutory blood-alcohol concentration (BAC) limits for driving in Germany are 0.50 g/kg (administrative offence) and 1.1 g/kg (criminal offence). Mass/mass concentration units (g/kg) are 6 % lower than mass/volume (g/L) units, because the density of blood is 1.06 kg/L on average. The corresponding statutory breath-alcohol concentration (BrAC) limits for driving in Germany are 0.25 mg/L and 0.55 mg/L, respectively. BAC in road traffic cases is determined indirectly by the analysis of serum and dividing by 1.236, which underestimates the true BAC. Using Alcotest 9510 DE evidential breath analyzer, the mean ± SD, median and range of BBRs of alcohol were 2047 ± 150, 2053, and 1571-2394 and 61% were less than 2100:1. After re-calculating BAC assuming a serum/blood distribution ratio of ethanol of 1.14:1 and reporting results in mass/volume units, the corresponding BBRs were 2220 ± 162, 2226, and 1703-2595 and 21% were less than 2100. Care is needed when the results of German studies of the BBR of alcohol are compared and contrasted with studies done in other countries.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".