Bibliographic record
Abstract
Researchers in Canada who examined the mobile phone bills of driv involved in accidents found that using a mobile phone while drivi quadrupled the risk of of a collision during the brief period of call. The UK Government has begun a consultation process on banni drivers from using handheld mobile phones. Some organiations cons that the use of hands-free mobile phones should also be banned. T Automobile Association (AA) also considers that there should be a crackdown on other distracting activities while driving such as consuming refreshments, smoking etc. Ireland proposed such a ban months age, but the garda (police service) expressed concern in c the communications systems in its enforcement vehicles did not co with the regulations. The UK Government considers that primary legislation is required to achieve a ban. A representative of the Irish Road Haulage Association has suggested that using a mobile phone to report delays can reduce driver stress and increase safe It is suggested that drivers use voicemail or messaging systems a take regular stops to check and answer calls. Hauliers point out there is often nowhere suitable to stop.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".