Human Settlement Quality Evaluation Based on Air Quality in Major Cities of China
Bibliographic record
Abstract
Based on the monthly data of 12 months in 30 major cities and combined with the monthly data of the air quality index (AQI) in 30 major cities, this paper analyzed the quality of human settlements in 2015 with the ArcGIS spatial analysis method. On the basis of the quality of air in this analysis, the coupling degree of the five great systems in these human settlements was also calculated. The finding shows that (1) according to the spatial distribution of the human settlements quality index in the main cities, the quality of urban human settlements was gradually decreased from the coastal regions to northwest inland regions, presenting an overall look that the quality was high in the south and low in the north, which converged to the change of the air quality; (2) the human settlements quality index apparently changed with season variation. A significant difference was found in the fourth quarter, and the biggest deviation among cities in China was up to 0.680, while the smallest deviation in the second quarter was 0.448; and (3) on the basis of factors that influenced the quality index, related kinds of norms in the main cities were built in the five great systems of human settlements, and the evaluated coupling degree between the five systems was at the antagonistic stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".