Railroad Right-of-Way Incident Analysis Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Locations of railroad right-of-way incidents in this research were identified as hotspots. These can be defined as highway-rail grade crossings or locations along the railroad right-of-way where collision or trespassing risk is unacceptably high and intervention is justified because the potential safety benefits exceed the cost of intervention. This project categorizes the hotspots as grade crossing and trespass incident hotspots. Mathematical models and theories are researched to see which ones may be used in identifying the hotspots. For the analysis of grade crossing incident hotspots, the Transport Canada model is modified to accommodate United States data and is applied to a sample of grade crossing incidents from 2003 to 2007 in the San Joaquin corridor in California. In analyzing trespass incident hotspots, the theory of cluster analysis, a type of spatial analysis, was researched. It appears that cluster analysis, used in conjunction with a geographic information system platform, would be a beneficial way of analyzing and predicting trespass hotspots.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it