Bibliographic record
Abstract
The City of Vancouver, Washington, located directly across the Columbia River from Portland, Oregon, is the second largest city in the region after Portland. Increasing traffic volumes created by economic and population growth in the City, and traveler delay exacerbated by recurrent and non-recurrent congestion have contributed to the transportation deficiencies in the region. The Vancouver Area Smart Trek (VAST) Program was initiated by the City and developed as a cooperative effort by several public transportation agencies in the region to enhance and integrate transportation mobility, efficiency, and safety through Intelligent Transportation System (ITS) solutions. Since communication is the backbone to the deployment of all other ITS elements, the City has decided to focus its initial ITS implementation activities on development of a robust communication system that will meet the City's needs and be consistent with the VAST Program. The design of the City's communication system not only requires the determination of the communication media, bandwidth requirements, and routing alternatives, but it also requires 1 consideration of the latest ITS-related communication solutions such as Ethernet capability, Internet-Protocol (IP) addressability, and conformity with National Transportation Communications for ITS Protocol (NTCIP). NTCIP is the focus of the System Requirements analysis because the primary purpose of NTCIP is to handle Center-to-Field and Center-to-Center communications, which are the two types of communications to be supported by the Vancouver communication system. Results of the Systems Requirements analysis were used in parallel with the VAST operational concept to develop the Concept of Operations for the City of Vancouver. This paper and the corresponding presentation will discuss the process of building the NTCIP communication framework for the City and describe how the Concept of Operations evolves to enable the integration of the Vancouver communication system with field devices and systems in the region.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".