Bibliographic record
Abstract
Existing geometric design guides provide deterministic standards for design requirements primarily based on near worst-case scenarios and conservative percentile selection of design parameters. Probabilistic geometric design analysis has been advocated to represent realistically the randomness in design parameters and variables. Probabilistic techniques can provide a measure of the degree of deviation from design standards. Collision modification factors have been advocated as quantitative measures of the impact on safety associated with changes in road features or traffic control. Often, however, no collision modification factors exist in the literature to predict the safety impact of changing particular road features. An important example is sight distance restriction on horizontal curves. Many highways in British Columbia, Canada, are located in mountainous terrain where the additional cost of earthwork or land acquisition to accommodate lateral road expansion can be prohibitive. In this constrained environment, a typical trade-off arises between design requirements (e.g., adequate sight distance on a horizontal curve) and budgetary constraint. The resolution requires comparing the consequences of every alternative. In these cases, reliability analysis can be used to evaluate the risk of deviating from the design requirements. A decision-support tool was developed to compare the risk of different deviations from sight distance requirements. Two case studies were used to investigate the safety implications of sight distance limitation on road segments, the risk associated with deviation from standards, and risk variations among the road segments. The proposed road design is associated with relatively high risk of limited sight distance, and the risk levels associated with standard design requirements vary significantly.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".