Spatio-Temporal Coupling Research on Urban Efficiency and Urban Development Degree in Northeast China
Bibliographic record
Abstract
In this paper, by using DEA model, urban development degree index model and the coupling degree model to estimate urban efficiency, urban development degree and coupling degree of 37 prefecture- level cities between years2003 to 2012. Analyzing the temporal and spatial evolution and exploring the relationship characteristics of urban efficiency, urban development degree and the degree of coupling between the two. The results showed that: 1)The coupling degrees of urban efficiencies and urban development degrees are not so high, and there is no significant improvement of the urban efficiency promoted by urban expansion. There is a significant diversity characteristics of the urban efficiency between the different function cities, the capital city showed input redundancy, resource based and traditional industrial city showed the shortage of input. 2)There is a negative correlation between urban development degree and urban efficiency in recent ten years, the level of economic development and the population density have promoted the urban efficiency, however, the urban constructional land area has negative relationship with urban efficiency. 3) The high matching level of urban efficiency, urban development degree and the degree of coupling between the two in space presents a more and more remarkable agglomeration characteristics.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".