Scenarios and tools for shipment planning and asset optimization for logistics clusters networks
Bibliographic record
Abstract
This deliverable addresses a methodological approach to develop and assess intermodal potentials within supply chains under real market conditions. For that intermodal planning tools are developed and tested using real data. The approach is based on the real intermodal services that are established within the CLUSTERS2.0 clusters and shipment orders as provided by shippers. A matching algorithm was developed in order to identify possible matches to use existing intermodal services. “For the potential analysis<br> “virtual” intermodal services were introduced on lanes with major volumes. The intermodal planning tool is part of the massification concept and supports shippers collaboration to exploit intermodal potentials with their supply chain through collaboration and coordination. Clusters 2.0 developed and applied an intermodal planning environment that is capable of assessing the potentials of collaborative demand planning of shippers as addressed in the massification concept. Making use of large-scale realistic demand data covering the area of the Clusters 2.0 clusters, a significant potential of existing and newly “massification” services could be identified. Our study provided a potential of up to 60% of all transport orders with distances higher than 250 km that can be operated by intermodal transport. The approach developed is supporting the activities as addressed in the massification workshops together with collaborating shippers.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".