Intoxilyzer® 5000C or Alcotest® 7410 GLC as Headspace Analyser for Alcohol in Beverages
Bibliographic record
Abstract
ABSTRACTA method of analyzing seized/suspected beverages for their alcohol content using the Intoxilyzer® 5000C or the Alcotest® 7410 GLC is reported. The accuracy of alcohol content on the labels of commercially sold beverages was also examined.Wines, beers and liquors (n = 21) bought from the open market were analyzed by headspace gas chromatography (GC). The data were compared with those obtained from using the Intoxilyzer® 5000C or Alcotest® 7410 GLC. With the latter two instruments, the headspace vapour of diluted beverages was blown into the calibrated instrument through the simulator. The results obtained by the latter two instruments were within ±6% of those obtained using the GC method.The labeled alcohol concentrations were within ±5% of the values measured by GC. They can, therefore, be used as the basis for blood alcohol concentration calculations.RÉSUMÉUne méthode d'analyse pour déterminer le contenu en alcool des boissons saisies/suspectes en utilisant l'lntoxilyzer® 5000C ou l'Alcotest® 7410 GLC est présentée. L'exactitude de la teneur en alcool indiquée sur les étiquettes des boissons commercialement vendues fut aussi examinée.Les bières, vins et spiritueux (n = 21) achetés sur le marché ont été analysés par chromatographie en phase gazeuse à pression de tête dynamique (CG). Les données furent comparées avec celles obtenues par l'utilisation de l'lntoxilyzer® 5000C ou l'Alcotest® 7410 GLC. Un simulateur fut utilisé pour introduire la phase vapeur des boissons alcoolisées diluées dans ces deux instruments. Les résultats obtenus par les deux instruments se situaient en deçà de ± 6% de ceux obtenus par la méthode CG.Les teneurs en alcool indiquées sur les étiquettes se situaient deçà de ± 5% des valeurs mesurées par CG. Ces valeurs peuvent donc être utilisées pour l'estimation théorique de l'alcoolémie.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".