Bibliographic record
Abstract
The purpose of this project was to design and deploy an Intelligent Transportation System (ITS) solution for ground transportation management at Vancouver International Airport. IBI Group designed and developed a Commercial Vehicle Dispatch System (CVDS) incorporating the following key features: (1) A central sub-system enabling the dispatch office to dispatch commercial buses, and manage the system using a web-based application; (2) A mobile sub-system enabling curbside personnel to dispatch taxis and monitor conditions using hand-held computers, software, and wireless communications; (3) A large eight-line variable message sign displaying dispatch requirements in the holding lot; (4) A vehicle detection system for automatically updating dispatch requirements on the sign. The resulting system has helped the airport to optimize its resources at the curb; lower passenger wait times through efficient dispatching; provide more detailed dispatch requests on the specific type of vehicle needed and the location they are needed at; minimize unnecessary oversupply, congestion, and idling at the terminals; and eliminate the reliance on a loud public address system. Partly funded by Transport Canada’s ITS Deployment and Integration Program, the project has successfully demonstrated the application of leading-edge, low-cost, and quickly deployable ITS solutions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".