THE INFLUENCE OF CANNABIS ON THE RISK OF ACCIDENTS: ANALYSIS OF SCIENTIFIC DATA AT INTERNATIONAL LEVEL
Bibliographic record
Abstract
In the majority of the studies carried out in the last ten years in Europe, the United States, Canada and Australia, the use of cannabis was identified in approximately 10% of the drivers injured or killed in a road accident, and sometimes more. Parallel to this study, the experimental studies also revealed the deterioration under the influence of cannabis of certain abilities necessary for driving a vehicle: reduced steering control; slower reaction times; impaired attention mechanisms and weaker or inappropriate responses in emergency situations. On a simulator or in a real situation, the effects are sometimes hardly even noticeable and can be more easily detected in the case of larger dosages. In certain situations, driers who have consumed a moderate dose and are aware that their abilities are modified will modify their behavior by taking fewer risks, such as keeping a greater distance from the vehicle in front of them or by reducing their speed. The adverse effects of cannabis on driving ability may therefore appear relatively slight in a normal situation. In contrast, there are situation where the influence of the consumption of cannabis can be very dangerous, such as emergency situations, monotonous long journeys and situation where cannabis is combined with other drugs, especially alcohol. This paper looks at the difficulties encountered when producing an epidemiological analysis and the hypotheses that researchers are moving towards.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".