Evaluation and Comparison of Size Relationship Between Port and Port-city:Taking the Port-cities of Liaoning Province as Example
Bibliographic record
Abstract
This paper studies the size relationship between port and port-city,and the research objects are the six port-cities of Liaoning Province,China.It tries to study the development level and the evolution characteristics of the port-city from the macroscopic and comprehensive perspective.The RCI index is employed to evaluate the size relationship between port and port-city,and then judge the port-cities′ category and evolution stage.Results show that: for one thing,there are three major port-city categories in Liaoning Province in 2008.Dalian is regional gateway city in large-scale and with the characteristics of typical port-city.Yingkou is remarkable regional gateway city.Jinzhou and Dandong are regional maritime cities which display port functions.Panjin and Huludao are general cities whose port functions are relatively weak.For another thing,in the recent ten years,the evolution of the size relationship shows four sides: first,Dalian is in the stage of developing from a gateway city to a typical port-city.Second,Yingkou has transformed from a typical port-city to a remarkable gateway city.Third,Jinzhou,Dandong,Huludao and Panjin have been developing slowly.Fourth,Yingkou and Dandong have realized the periodical leap-frogging development.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".