Bibliographic record
Abstract
Ross, Patel, and Wenzel reply: These two thoughtful letters illustrate the importance of myriad details of vehicle design to dangers and safety in traffic. The height of the lights of most SUVs and trucks, which temporarily blind car drivers at night, is a significant risk (which has been crudely quantified in fatality statistics as around 100 per year). Ian Halliday’s comments about daytime running lights are indirectly supported by the impressive fatality reductions that are being achieved in Canada (see figure 2 of our article). Those reductions should inspire Americans to question the less-than-impressive claims of success made for US traffic safety programs. Vehicle design is critical to traffic safety. Specific design features, such as the heights of car seats versus the heights of “truck” fronts, where the trucks are merely serving as car substitutes, are among the most important issues for safety design. Differences in vehicle structures are important; but as we argued in our article, the laws of physics do not imply that vehicle mass, as such, is a safety feature. Observation suggests it is relatively unimportant in today’s fleet.© 2006 American Institute of Physics.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".