Land Use Change and Ecological Environment Effects of Resources-based Cities——A Case Study of Baiyin City in Gansu Province
Bibliographic record
Abstract
Industrial transformation of resource cities due to the exhaustion of mineral resources affects the urban land use and eco-environment situation.The coordination of development relationship between land use and eco-environment has an important meaning for changing the traditional urban land use pattern and promoting the harmonious development between human and nature.With 1990,2000and 2011,3periods landsat TM satellite remote sensing images of Baiyin resources city as the main data source,land use change characteristics and effect on the ecological environment were analyzed and evaluated based on the ENVI software from 1990to 2011,and using several kinds of mode,such as single land use type dynamic degree model,dynamic transfer matrix,regional ecological environmental quality index model,regional ecological environmental quality contribution index model.The results showed that construction land area has been growing,cultivated land area firstly increased and then decreased,unused land area has been in reducing trend,woodland,grassland,water area changed little because of their small initial area in Baiyin city during 21years;ecological environment quality will develop toward a healthy state overall because ecological environment quality index increased from 0.087to 0.126;contribution rates of cultivated land and unused land were the highest to ecological environment quality improvement,while contribution rates of forestland and water were the highest to ecological environment quality degradation in BaiYin city where cultivated land,unused land water were key factors affecting the regional ecological environment change,and cultivated land was the dominant factor among all factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".