Measuring Water Transport Efficiency in the Yangtze River Economic Zone, China
Bibliographic record
Abstract
Water transport, a component of integrated transport systems, is a key strategic resource for achieving sustainable economic and social development, particularly in the Yangtze River Economic Zone (YREZ). Unfortunately, systematic studies on water transport efficiency are not forthcoming. Using Data Envelopment Analysis (DEA) and the Malmquist index as a model framework, this paper measures water transport efficiency in YREZ, conducts spatial analysis to identify the leading factors influencing efficiency, and provides scientific evidence for a macroscopic grasp of water transport development and the optimization of YREZ. The results indicate that water transport technical efficiency (TE) in YREZ is low and in fluctuating decline. Therefore, it has seriously restricted performance and improvements in the service function. Additionally, the spatial pattern of TE has gradually changed from complexity and dispersion to clarity and contiguity with a larger inter-provincial gap. Water transport efficiency has slightly improved through technological change (TECHch), whereas deteriorating pure technical efficiency change (PEch) is the main cause of a TE decrease. According to our findings, decision-makers should consider strengthening intra-port competition and promoting water transport efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".