Bibliographic record
Abstract
Greyhound has recently equipped its fleet of 140 Prevost X3-45 motorcoaches with a new model of belted seats that feature lap-shoulder belts and a new technology that offers full compartmentalization protection in frontal crashes, even for unbelted passengers. There are two parts to the seat: the inner structure that provides the lap-and-shoulder belts and absorbs the crash energy for the belted passenger, and an outer seatback that remains vertical and cushions the impact of any unbelted passenger sitting behind it. The first coaches with the new seats are assigned to the New York-to-Montreal, New-York-to-Toronto, and New-York-to-Boston routes. Greyhound eventually plans to install them on all its routes nationwide. While the federal government does not yet require seat belts in large buses, Greyhound is the first to do so, addressing one of five federal highway safety priorities on the National Transportation Safety Board (NTSB) Most Wanted List of Transportation Safety Improvements for 2008. The company that owns Greyhound has worked with the new seat’s manufacturer. Developing the protocol for the seats involves four steps. The first is to understand the problem: how are passengers being killed or injured in crashes? The second is to evaluate the demographics of the bus riders. In Greyhound’s case, as opposed to yellow school buses, there is a wide spread of potential riders, in terms of size and age. Thirdly, the protocol must be based on an assessment of the current level of safety, including the crash forces that passengers experience. The final step is to consider how often passengers use belts. With Greyhound taking the lead, other smaller companies are following suit, though implementation for smaller operators will take time because of the high costs involved.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".