Bibliographic record
Abstract
In the United States, drivers impaired * by alcohol and/ordrugs are responsible for more than 16,000 deaths, onemillion injuries, and $45 billion in costs annually.1 As part of the attempt to reduce these human and economic tolls, law enforcement officers routinely conduct tests of eye movements to determine if a driver is under the influence of alcohol or other drugs. Alcohol, other central nervous sys-tem (CNS)-depressant drugs, inhalants, and phencyclidine (PCP) and its analogs will affect the neural centers in the brainstem and cerebellum, which control eye movements, as well as other motor, sensory, and cognitive integration areas of the brain. In addition, certain antihistamines have physiologic and cognitive effects similar to CNS-depressant drugs. Blood alcohol concentration (BAC), also known as blood alco-hol level, is either measured directly from a blood sample or estimated from a breath or urine sample. BAC is com-monly reported as a percentage of alcohol weight per vol-ume of blood. When impairment is due solely to the influence of alcohol, most states and Canadian provinces define the legal limit for passenger vehicle drivers as 0.08%, while some states still allow the higher limit of 0.10%. Positive findings on the Horizontal Gaze Nystagmus (HGN) test have been shown to correlate highly with both BAC and cognitive impairment.2 The American Optomet-ric Association has previously recognized the validity and reliability of the HGN test as used by the law enforcement community.3 Nystagmus testing in intoxicated individuals
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.769 | 0.596 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".