ROADIDEA INCO – A Comparison of North-American and European Data Availabilities and Applications
Bibliographic record
Abstract
The main target of ROADIDEA, a collaborative research project in the 7th Framework Pro-gramme of the European Union, was to answer the question, whether the European Transpor-tation System is still able to produce radical innovations. And if not, what are the barriers to be overcome. Amongst other side aspects of such a general investigation of innovation potential, the data aspect was one of the most important investigations carried out in parallel. In a dedicated data work package corresponding data availability and related aspects were analysed. However, the heterogeneous availability of necessary data (traffic and weather) in Europe was identified as one of the main barriers for the implementation of desired services on a conti-nental scale. This specific barrier made it almost impossible to implement similar services in different European countries and regions. When reporting this to the European Commission, the question came up, whether a free data policy – as assumed being applied in the U.S. – could solve this issue. As a minor extension to the ROADIDEA project, ROADIDEA INCO (International Cooperation Aspects) was charged with the investigation of comparable ITS services in the U.S. and Canada and underlying data. But also the other way around approach – to investigate available data and assess corresponding application and services - promised to reveal interesting relations between available data and derived applications and services. This paper describes the results of the data source and application investigation of the ROADIDEA INCO project.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".