Bibliographic record
Abstract
The European road network is very large, and much of it is made up of two-lane rural roads. In Italy the road network requires adjustment work to improve mobility safety management. One of the most important tools for this analysis is the operating speed (V85) profile. In many design standards, different formulations are used for estimating V85, obtained on the basis of research carried out at different times and in different contexts. The aim of the research presented in this study is to analyze driver speed behavior on two-lane rural highways. The study was conducted using traffic counters, able to record in both directions and for every passage of a vehicle, its length, instant speed and direction. The survey plan was elaborated to satisfy different research objectives. The readings were taken by keeping every section under observation for 3, 6 or 12 hours. The data were collected on 11 homogeneous sections of highway. The database consisted of free-flow passenger car speeds and various geometric data from 103 sites. Data were collected at sites to both develop and validate equations. The proposed model includes the radius of horizontal curvature and the “Speed Environment”, which is defined as the speed at which users travel in free-flow conditions when they are not constrained by the alignment of the highway.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".