Study on Progress of Developing Strategy on Ports Cluster: Integration of Port Resources
Bibliographic record
Abstract
Ports play important roles in linking transportation routes and distributing cargo, which promote the national and regional economy and trade development. Although ports formerly dominated the distribution and transportation of cargo in the era of sailing boats all over the world, their monopoly has been challenged more and more fiercely during the process of economic globalization and integration. Under the current economic conditions, the competition or opposition between the ports are not suitable for their normal operation. In order to adapt to the ongoing reformation of sea transportation, it is inevitable that port resources integrate to take advantage of complementary cooperation. As the integration of resource can optimize the allocation of the limited port resources, it brings about overall advantage to promote the development of port cluster and regional economy. However, a lot of problems would still need to face in the practical process. To meet the requirements of economic and social development, the researchers have launched a large number of theoretical and practical researches in their field about the development of port resource integration. So, the theoretical and practical researches in the published papers are studied to open out the significances of port resource integration and seek after the solution to the problems in development of ports in the present study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".