PEDESTRIAN FATAL CRASHES ON FREEWAYS IN TEXAS
Bibliographic record
Abstract
Over the five-year period of 2007 through 2011, 2,232 fatal pedestrian crashes were recorded in Texas. 21% of these crashes were found to have occurred on controlled-access facilities (i.e. freeways). This is an alarmingly high number for a location where pedestrians are least expected. This study analyzed crash reports and police officer narratives to understand the characteristics and contributing factors associated with fatal pedestrian crashes on freeways. The contributing factors identified include pedestrian and driver alcohol use and dark conditions. Eighty percent of the crashes occurred after dark, almost half of which were at a location with no lighting. Intoxicated pedestrians were involved in twenty eight percent of crashes, with an average BAC of 0.20. To alleviate this problem, there may be a need for conducting a “Don’t Drink and Walk” campaign to educate the general public of dangers of walking while intoxicated, especially at night. A quarter of the crashes involved unintended pedestrians, i.e. those who were out of their vehicle due to a previous crash or a stalled vehicle. Motorists should be educated to not work on their vehicle in traffic and not to try and cross the freeway to reach a shoulder or median. It is best to wait in the vehicle with seat belt and hazard lights on until emergency services arrive.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".