Ontario Commercial Vehicle Survey: Use of Geographic Information Systems for Data Collection, Processing, Analysis, and Dissemination
Bibliographic record
Abstract
The Ontario Commercial Vehicle Survey (CVS) is part of the National Roadside Study (NRS) conducted by Transport Canada about every 5 years across Canada on major highways and international border crossings. The NRS is a roadside truck driver intercept survey that captures many aspects of the trip, including route, commodity, vehicle weight and dimensions, and driver and carrier profile. In the past 10 years, significant improvements have been made in data collection, processing, and reporting techniques to enhance the accuracy of the survey data. The direct data entry method was introduced in 1995, followed by data processing and reporting techniques based on geographic information systems (GIS) in the 1999–2001 survey. The ongoing 2005–2007 survey software includes a GIS-based routing component that will enable the surveyor to confirm the route with the driver and modify it, if required, to get an accurate profile of the highways used for the trip. Currently the CVS is the most detailed source of intercity commercial vehicle characteristics and commodity flow information available to the Ontario Ministry of Transportation (MTO). The data have been used by various levels of government and private-sector consultants for studies to prioritize multiyear strategic investments.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".