Research on Relationship between Possession of Private Vehicles and Air Quality in China
Bibliographic record
Abstract
This paper deals with the relationship between possession of private vehicles and air quality, applying panel data model and based on the data in 31 provinces of China from 2003 to 2009. The research shows that there is hardly positive correlation between them in the provinces in China. On the contrary, the correlation between them is negative in most of the provinces. As a result, we conclude that private vehicles don’t cause serious air pollution and there must be other more essential factors that cause air pollution. Key words: Possession of private vehicles; Air quality; Panel data model Resume: Cet article traite de la relation entre la possession de vehicules prives et la qualite de l'air, en utilisant le modele de groupe de donnees et base sur les donnees dans 31 provinces chinoises de 2003 a 2009. La recherche montre qu'il n'y a guere de correlation positive entre eux dans les provinces en Chine. Au contraire, la correlation entre eux est negative dans la plupart des provinces. En consequence, nous concluons que les vehicules prives ne causent pas de pollution de l'air grave et il doit y avoir d'autres facteurs plus essentiels qui causent la pollution de l'air. Mots-cles: Possession de vehicules prives; Qualite de l'air; Modele de groupe de donnees
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".