D9.5 Public recommendations for inland transport in Northern Europe
Bibliographic record
Abstract
"Public Recommendations for Inland Transport in Northern Europe" serves as a comprehensive guide, providing practical insights for policymakers and industry stakeholders striving for a sustainable and efficient inland waterway transport (IWT) system. Specifically focusing on the objectives of the AEGIS project's Use Case B, led by DFDS, supported by Aalborg University (AAU) and the Technical University of Denmark (DTU), the report underscores the crucial need to optimize transportation activities within the European waterborne transport sector. By advocating for the adoption of zero-emission propulsion systems and innovative vessel designs, the report aims to facilitate the seamless integration of eco-friendly practices within the IWT framework. It highlights the importance of customized shuttle sizes and efficient cargo transshipment processes to ensure the smooth movement of goods between ports and terminals. The recommendations stress the significance of cultivating collaborative partnerships and standardized protocols to enable streamlined coordination and data exchange across various transport modes, fostering a cohesive and sustainable logistics network. With a strong focus on sustainable freight corridors and public awareness campaigns, the report emphasizes the advantages of IWT, including reduced carbon emissions, cost-effectiveness, and employment opportunities. It underscores the critical role of regulatory enhancements and the allocation of dedicated funds for infrastructure development, climate resilience, and the advancement of green technologies. Aligned with the goals of the European Green Deal and the broader sustainability objectives of the European Union, these recommendations aim to nurture a resilient and environmentally conscious inland waterway transport system, paving the way for a more sustainable future in European maritime logistics.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.018 |
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".