Analysis, modeling, and assessing performances of supply chains served by long-distance freight transport corridors
Bibliographic record
Abstract
This article deals with an analysis, modeling, and assessing performances of supply chains served by long-distance intercontinental intermodal rail/road- and sea-shipping freight transport corridor(s). For such a purpose, the supply chains are defined and the methodology for assessing their performances under given conditions is developed. The methodology consists of the analytical models of indicators of the operational, economic, environmental and social performances of particular corridors and corresponding supply chains assumed to be dependent on the infrastructural and technical/technological capabilities. The models of particular indicators have been applied according to “what-if” scenario approach to assessing performances of the long-distance intercontinental inland and maritime freight transport corridors spreading between China and Europe in the scope of the “Silk Road Economic Belt” and “A New Maritime Silk Road” policy initiative. The results prove that the intermodal inland rail/road alternative could act as a serious competitive alternative to its maritime deep-sea counterpart under given conditions. Nevertheless, in order to realize the opportunities, large investments in the inland rail/road infrastructure are required to appropriately connect China with Europe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".