Bibliographic record
Abstract
Abstract The standard field sobriety test (SFST) is used in many jurisdictions to detect whether drivers are impaired by alcohol or drugs. Often alcohol is first determined in breath to exclude alcohol as a cause of impairment. The SFST is mostly performed by trained police officers, in some cases by physicians. The SFST can consist of many tests, but generally includes horizontal gaze nystagmus, walk‐and‐turn, and one‐leg stand. The drug evaluation and classification program in the United States and Canada has 12 steps, and lasts approximately 1 h. On the basis of this information, the drug recognition expert (DRE) determines whether a suspect is impaired, whether the observed impairment is due to drugs, and which category (or categories) of drugs might be responsible. There are seven categories: central nervous system (CNS) depressants, CNS stimulants, hallucinogens, phencyclidine, narcotic analgesics, inhalants, and cannabis. Although these tests are relatively reliable in determining whether someone is impaired and the cause of the impairment, they lack sensitivity, and recent studies have shown that the SFST was not sensitive to clinically relevant driving impairment caused by several drugs such as tetrahydrocannabinol or methamphetamine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".