Extended Land-Use Coding System and Its Application in Urban Brownfield Redevelopment: Case Study of Tiexi District in Shenyang, China
Bibliographic record
Abstract
Management of land use related to brownfield redevelopment areas offers opportunities to respond to the challenges from rapid urbanization in China. This paper explores the mixed functions of land use in brownfield redevelopment by using the Tiexi District in Shenyang as a case study and extending the current national land-use coding system. Based on examination of the land-use statistics for the Tiexi District, the authors found that the current coding system is not suitable or precise enough for calculating the areas of land use or its mixed-function-based measurement in a way useful for scientific research and local policy-making. Thus, an extended coding system is proposed, and four examples (two residential communities, one commercial business facility, and one industrial cultural plaza) were selected for empirical study. The extended coding system supplies more-detailed information for understanding the land-use functions in brownfield redevelopment and provides a more-valid database for measuring the social, economic, and environmental implications of redevelopment of brownfield lands. Extended land-use categories also should benefit local decision-making regarding long-term sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".