Bibliographic record
Abstract
Intelligent Traffic Systems have traditionally used dedicated wire or wireless communications services. Cellular service providers are rapidly moving to deploy all digital networks in order to provide advanced cellular services to their subscribers. The personal communication services (PCS) have been introduced in major urban centres in both the US and Canada with expansion along major interstate corridors. These new digital networks provide both voice and data communications which can overcome the capital costs and deployment time of dedicated facilities. Civil construction, to provide local cable access to subsystem traffic controllers, is not required resulting in lower capital costs and rapid deployment. Recurrent costs are usage based and distance independent allowing for wide area deployment. The characteristics of the PCS network differ from traditional copper or fiber optic circuits used in ITS systems. The PCS network provides its own addressing scheme assigning dynamic IP addresses while legacy traffic controllers utilize fixed addresses with a poll - response access method. The continuous growth of ITS from urban to rural areas creates a need to manage the subsystem traffic controllers by a more efficient means. This paper examines the suitability of implementing PCS for ITS application addressing issues such as integrating with legacy traffic controllers, communication protocols, network latency and identifying network limitations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.055 |
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".