A study on the influencing factors of ambient air quality index and its measures in Zhangye city
Bibliographic record
Abstract
This study is based on the PM2.5, PM10, CO, NO2, SO2, O3, precipitation, and temperature data of Zhangye City from 2016 to 2022 and uses the factor analysis model and multiple regression analysis method to demonstrate the variable relationship between the environmental air quality index (AQI) and its factors in Zhangye City, aiming at understanding the relationship between air quality and its influencing factors. The results of the study show that the independent variables NO2, O3, precipitation, and temperature have a significant impact on the dependent variable AQI, and a multiple linear regression model is established, and the fitting degree of the regression equation is good. This study uses the latest data, which can more effectively reflect the actual situation of air quality in Zhangye City and the dynamic changes of influencing factors compared with previous studies. In addition, this study comprehensively considers the impacts of various air pollutants as well as meteorological factors such as precipitation and temperature on air quality and is able to reveal the complex relationship between air quality and its influencing factors more comprehensively and deeply.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".