Creating a common European mobility data space
Bibliographic record
Abstract
Transforming Europe into a climate neutral economy by 2050 in line with the European Green Deal places a particular responsibility on the transport sector, which accounts for a quarter of the Union’s total greenhouse gas (GHG) emissions. Specifically, transport will have to collectively reduce its GHG emissions by 90% by mid-century compared to 1990 levels. This will require advancing digitalisation and the use of data in all modes of transport, including passenger and freight segments. Notwithstanding, data availability, access and exchange in the transport sector today continue to be hampered due to unclear regulatory conditions, the lack of an EU market for data provision, the absence of an obligation to collect and share data, incompatible tools and systems for data collection and sharing, different standards, or data sovereignty concerns, among others. The European strategy for data aims to establish a single market for data, where data can be accessed and used efficiently. This will include the creation of a common European mobility data space to facilitate access, pooling, and sharing of transport and mobility data. Against this backdrop, the 10th Florence Intermodal Forum brought together relevant stakeholders to discuss opportunities and challenges for building such a mobility data space.
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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 | 0.040 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".