Precision of Breath Alcohol Testing in the Field using the Intoxilyzer® 5000C and the Paradox of Truncation
Bibliographic record
Abstract
In a retrospective study, Intoxilyzer® 5000C results obtained over a four year period by qualified breath technicians of the Toronto Police Services (TPS) between 1995 and 1998 were analysed and compared for the precision between untruncated and truncated duplicate breath tests. A total of 8585 breath tests were analysed. Of these, 8309 (97%) were found to be within 20 mg/dL untruncated, versus 276 (3%), that were within 21 to 29 mg/dL. The mean absolute (unsigned) difference between subject tests that were within 20 mg/dL untruncated for all four years was 7.05 ± 4.91 mg/dL (mean ± sd). After truncation, only 178 or 2.07% of the tests that differed by an absolute value of 21–29 mg/dl were rendered 20 mg/dL apart. Most of the rejected values were from the range of 25–29 mg/dL different, skewing the acceptable data significantly towards the 21–22 mg/dL range. Truncation thus does not randomly render acceptable differences between breath tests of 21 to 29 mg/dL and this procedure retains scientific relevance and does not unfairly prejudice drinking drivers. These results reveal that properly trained qualified breath technicians operate the Intoxilyzer® 5000C with a high degree of precision, resulting in 97% of all paired subject tests being within 20 mg/dL of each other using the untruncated values.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".