Bibliographic record
Abstract
Land is not only a major space for human production and living, but also one of the most precious resources to humans. As a space carrier of urban construction, urban land resources constitute the part with the highest asset benefit among land resources, offering an essential space for economic reproduction, population reproduction and environment reproduction in urban areas. To sum up, urban land resources are the material basis, guaranteeing sustainability of urban development.In this paper, changes of sustainability of land use in Zhengzhou City, Henan Province from 2011 to 2015 were analyzed so as to evaluate sustainability level of land use in Zhengzhou. Based on correlation analysis, resource, environment, economy and society were selected as four evaluation indexes, and their weights were determined. Then, the method of maximum was used to realize data normalization, and the comprehensive index value was computed. Finally, sustainability of Zhengzhou’s land use was comprehensively evaluated. Taken as a whole, sustainability of Zhengzhou’s land use was improving from 2011 to 2015, but the comprehensive sustainability level was still low, calling for further strengthening. From 2014 to 2015, the sustainability level of land use was still on the downward.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".