Use of Safety Components to Avoid Accidents With Agricultural Tractors in Public Roads
Bibliographic record
Abstract
The market of agricultural tractors has an important role in the Brazilian economy, as well as the role the tractor plays in agricultural operations. With the rising level of mechanization, the traffic of tractors on public roads consequently increased, thus raising the propensity of occurring accidents. In transit, tractors present low traveling speed, besides being wider machines in comparison to cars, besides also presenting low visibility to the other drivers who use the roads. The relevance of studies that point the problems related to this type of traffic accident is related to its severity, in order to seek preventive measures. In this context, this study aimed to address the interface of safety components related to lighting and signaling with the avoidance of accidents involving agricultural tractors on public roads. This way, studies show aspects such as: the road speed limit, as well as its type and width; the number of vehicles and agricultural machinery in circulation; safety components; lighting and signaling items, influence and help to draw a characterization of accidents involving farm machinery. Among the types of accidents, collision and overturning are the most common. Even if the number of accidents with tractors is lower in relation to automotive vehicles, the severity of the accidents is greater, with propensity of 5 to 8 times more deaths. Therefore, the correct use of safety components and items of lighting and signaling on tractors, in addition to the compliance with laws and regulations, may contribute to reducing the number of accidents with agricultural machines on public roads.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".