FIFTY YEARS ON - LOOKING BACK AT DEVELOPMENTS IN METHODS OF BLOOD- AND BREATH-ALCOHOL ANALYSIS
Bibliographic record
Abstract
Exactly 50 years ago at the T-1950 conference in Stockholm, a new principle was introduced for measuring alcohol in biological specimens. This involved the oxidation of ethanol with an enzyme called alcohol dehydrogenase (ADH), which had been extracted and purified from horse liver. The ADH method was more sensitive and selective for measuring ethanol than the wet-chemistry oxidation procedures used during the first half of the century. Breath tests for alcohol were given a boost when Borkenstein developed the Breathalyzer in 1954. Breath testing for alcohol influence became widely used for traffic law enforcement purposes in USA, Canada, and Australia. In European countries blood and urine were the preferred specimens for forensic alcohol analysis and by the early 1960s the method of gas chromatography (GC) appeared including the headspace sampling technique, which was perfect for analyzing volatile substances in body fluids. Interest in Europe shifted towards evidential breath-alcohol testing in the 1980s, which coincided with the introduction of new analytical technology for sampling and analysis of breath, such as compact infrared (IR) spectrometers controlled by microprocessors. In the 1970s, electrochemical oxidation of alcohol with fuel cell devices became popular and these were incorporated into hand-held instruments suitable for roadside screening of motorists. Recent improvements in this kind of technology have meant that fuel cells are being used for evidential breath-alcohol testing. Whether breath-alcohol devices utilizing gas chromatography and mass spectrometry (GC-MS) or Fourier transform infrared spectrometry (FTIR) will emerge to provide the ultimate way of identifying ethanol in blood and breath samples for forensic purposes remains to be seen. One goal for the new millennium, at least in some countries, seems to be the use of evidential breath-alcohol testing at the roadside. This saves much time and resources for the police and also reduces the number of false-positive roadside alcohol screening tests. For the covering abstract see ITRD E106992.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".