LITERASI INFORMASI MAHASISWA PROGRAM STUDI ILMU PERPUSTAKAAN FAKULTAS ILMU SOSIAL DAN ILMU POLITIK UNIVERSITAS SEBELAS MARET SURAKARTA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alcohol and cannabis are the two most commonly found intoxicating substances in fatally injured drivers. Epidemiological studies have demonstrated that the use of alcohol or cannabis can lead to an increase in the risk of a motor vehicle collision. Reducing the risks associated with driving under the influence of alcohol or cannabis is achieved partly through roadside detection of breath alcohol concentrations (BrAC) or blood delta-9-tetrahydrocannabinol (THC) levels. The purpose of the present review is to compile the laboratory studies on the combined effects of alcohol and cannabis on simulated driving as well as those evaluating combinations of these drugs on BrAC or blood THC. Given that driving can be affected by a number of cognitive processes, the literature on the cognitive effects of combinations of alcohol and cannabis is also reviewed, along with a discussion of a potential additive effect on the subjective qualities of these drugs. In sum, it is concluded that alcohol and cannabis have additive effects on driving skills, cognition and subjective effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it