The Extension Characteristics and Dynamic Mechanisms of Residential Space in Changchun since 1990
Bibliographic record
Abstract
Based on the 1995, 2003, 2008 years' land utilization present situation chart, the 2011 year remote sensing images of Changchun city and related statistical yearbook, combining with GIS software and SPSS statistical tools,utilizing various measure methods, this article mainly studies on the extension characteristics of residential space in Changchun city. The conclusions show that:(1)The overall extension mode of residential space is given priority toenclave type + shaft type, but different stages have different characteristics;(2)The whole development of residential space has experienced large- scale development process in different directions and stages;(3)At the present stage, the residential space is mainly focused on the core area of Changchun city, but the characteristic of gradient distribution has appeared significantly and the suburbanization has already occurred. On the basis of the above research, the paper builds the index evaluation system of the dynamical mechanism of residential space extension and uses a method of the qualitative and quantitative analysis to study the influence degree of the specific factors force. Its dynamic mechanisms including the push of industrialization, the increase of the level of economic development, the improvement of traffic conditions, the regulation of government, the reform of land system and the function of the urban planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".