Bibliographic record
Abstract
This technical paper highlights the ten year evolution of the I-95 Corridor Coalition - an alliance of transportation agencies, toll authorities, and related organizations, including law enforcement, from the State of Maine to the State of North Carolina, with an affiliate member in Canada. The Coalition provides a forum for key decision and policy makers to address transportation management and operations issues of common interest. This volunteer, consensus-driven organization enables its myriad state, local and regional member agencies to work together to improve transportation system performance far more than they could by working individually. The Coalition has successfully served as a model for multi-state/jurisdictional interagency cooperation and coordination for over a decade. It began in the early 1990's as an informal group of transportation professionals working together to more effectively manage major highway incidents that impacted travel across jurisdictional boundaries. In 1993, the Coalition was formally established to enhance transportation mobility, safety, and efficiency in the region. During the late 1990's, the focus of the Coalition's program evolved from studying and testing intelligent transportation systems (ITS) technologies to a broader perspective that embraced integrated deployments and coordinated operations. The Coalition's perspective also evolved from a highway concentration to one that encompassed all modes of travel and focused on the efficient transfer of people and goods between modes. The facilitation of regional incident management in areas such as preplanning, coordination and communication among transportation and public safety agencies in the corridor have remained as a key part of the Coalition's focus. Today, the Coalition emphasizes information management as the underpinning of seamless operations across jurisdictions and modes. The Coalition's ten-year partnership is strong, real and growing. As its evolution continues, the next decade holds many more challenges and opportunities for the Coalition in improving the transportation system.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".