Bibliographic record
Abstract
Motor vehicle accidents remain a major cause of death and injury despite recent reductions related to enforcement of speed restrictions and maximum blood alcohol levels. In 1996 there were over 2000 fatalities and approximately 20,000 serious injuries in Australia from motor vehicle accidents (Ferguson, Amoako et al. 2000). In Australia road traffic accidents are the seventh highest cause of years of life lost due to premature mortality (Van Der Weyden 1999). Alcohol, excessive speed, inexperience and sleep related fatigue (sleepiness) have been implicated as major causes of motor vehicle accidents. The Canadian Expert Panel stated "Driver fatigue has long been recognised as a major risk factor for commercial drivers. Estimates of the percentage of crashes that are partially or completely attributable to fatigue range from 1 to 56 percent, depending on the database examined, the level of detail gathered from crash investigations, and the study methodology employed” (Vespa 1998). Sleep related fatigue impairs reaction times, vigilance and peripheral vision, and ultimately results in falling asleep inappropriately (Akerstedt 1988) (Dinges, Pack et al. 1997) (Williamson, Feyer et al. 1996) (Russo, Thorne et al. 1999). These periods of falling asleep are initially very brief “microsleeps”, but as fatigue increases longer periods of sleep occur. These microsleeps can result in accidents due to failure to respond appropriately to the environment, such as obstacles and adjusting steering and speed. Impaired performance due to driver fatigue has been demonstrated on simulated driving tasks (Nilsson, Nelson et al. 1997). Fatigue and microsleeps cause speed variability and an increase in lane drift, which can result in drifting into an adjacent lane or off the road, resulting in accidents (Riemersma, Sanders et al. 1977).
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".