D2.3 Intelligent operations systems and new technologies for intermodal logistics optimization
Bibliographic record
Abstract
The present report is the Deliverable from task 2.3 of the ADMIRAL – Advanced Marketplace for Low Emission and Energy Transportation project, funded by the European Union under the HORIZON-CL5-2022-D6-02 with Grant Number 101104163.ADMIRAL WP2 – Sustainable development of logistics & transportation addresses key sustainability issues in the transportation and logistics sector such as zero (low) emissions logistics, reduction of energy consumption from fossil fuels in transportation and enhancement of collaborative logistics to reach common sustainability goals in the pilots to be implemented in Finland, Lithuania, Portugal-Spain and Slovenia-Croatia.The present report «Intelligent operations systems and new technologies for intermodal logistics optimization » is one result of task 2.3 - Current (mega) trends for sustainable logistics, which integrates ADMIRAL WP2 - Sustainable development of logistics & transport. Following ADMIRAL’s project Grant Agreement 101104163, the main goals of task 2.3 are as follows: • To identify global trends on innovative solutions to improve the sustainability performance of operations (Reverse logistics, Symbiotic logistics, etc.).• To identify how companies/stakeholders are dealing with identified technological changes and adapting systems for digitalisation, automation and the creation of new services (IoT, autonomous delivery, robotics, circular supply chains, etc.).• To analyse how the requirements for improving resilience and sustainability at the same time are considered and should be considered in the future.• To identify/assess how intelligent systems are being used or planned to integrate all logistics stakeholders (producers, suppliers, ship owners, transport operators, support services, etc.), including sustainability performance indicators.• To analyse how governance practices connect all levels of suppliers and service providers considering code of conduct and corporate reports to achieve sustainability goals.• To map innovative solutions, technological and social, identifying the contribution of each for a more efficient and sustainable supply chain (e.g., autonomous vehicles and delivery, factory ships with product finishing (customization), including industry 5.0 issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".