Bibliographic record
Abstract
The goal of the Ministere des Transports du Quebec is to ensure the movement of people and goods throughout its territory by maintaining a safe and efficient transportation system. In order to fulfill its role effectively, the Ministry must ensure that it knows the condition of its road network in real time, so that it is managing appropriately the teams carrying out maintenance, and that it can respond quickly to any emergency. To carry out its mission, the Direction de l'Estrie has developed a state-of-the-art vehicle communication system combining technology and human know-how which allows it to handle Quebec winters more effectively. It has therefore established an Integrated Monitoring Center supplied with precision equipment describing weather and road conditions together with a monitoring service for the network which is in continuous operation. The Centre will thus be able to use intervention strategies that will optimize snow and ice removal. To ensure the success of the project, the Direction de l'Estrie has integrated its precision tools, especially the on-board computers equipped with GPS and software with specific applications installed in patrol vehicles and spreading trucks, as well as communication systems which receive data off-line (short-range WI-FI antennas) and in real time (cellular technology). This information is supported by a WEB service that allows data to be visualized using cartographic and geomatic techniques. By encouraging the sharing of research information, the pilot project and the technology now being implemented have also promoted the exploration of new ways of improving the performance of this organization in its management of the road network as well as heralding a promising future for these technologies in the marketplace. For the covering abstract see ITRD E143097.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.046 |
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".