Comprehensive Evaluation of Eco-environmental Quality in Guanzhong Urban Agglomeration Based on Multi-source Remote Sensing Data
Bibliographic record
Abstract
With the rapid development of social economy in Guanzhong urban agglomeration, the living standards of the people has been greatly improved. Because of the extensive mode of development, environmental problems have also become the cost of economic development in Guanzhong urban agglomeration. Urban agglomeration is facing a series of environmental problems, such as ecological destruction, atmospheric pollution, water shortage and so on. Focusing on these issues, multi-source remote sensing data and auxiliary data are integrated to build a comprehensive and regional eco-environmental quality assessment model. The evaluation model is established based on the combination of Fuzzy Analytical Hierarchy Process, Principal Component Analysis and Lagrange Multiplier. The results show that the quality of ecological environment in Guanzhong urban agglomeration shows a downward trend in general. The area of the eco-environmental quality index between 0.4-0.6 performs an upward trend, which transformed from other levels. The quality of ecological environment in Guanzhong urban agglomeration has gradually improved from north to south. The southern part of Guanzhong urban agglomeration is Qinling Nature Reserve, which Contains a lot of woodland and has high ecological environment quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".