Analysis on the variation trend and influence factor of ambient air quality in Xiaoshan district
Bibliographic record
Abstract
The environmental monitoring data of air pollutants in Xiaoshan district during 11th Five-Year Plan period(from 2005 to 2010) were evaluated by air quality comprehensive pollution index,air pollution index(API) and Daniel trend test method to study the variation trend and influence factor of air quality.Result showed that PM10 was the primary pollutant of atmosphere pollution in Xiaoshan district;the concentration of SO2,PM10 and the value of comprehensive pollution index were increased first and decreased,they presented a generally insignificant downward trend during the year 2007 to 2010,indicating the air quality was improved after 2007.The NO2 pollution load was rising year by year;the correlation analysis results and NO2/SO2 ratio suggested that the air pollution pattern in Xiaoshan district was transferring from coal burning pattern to mixed pattern.The monthly average API value was highest in the fourth quarter,and the following in order was the first quarter,the second quarter and the third quarter.Environmental protection policies such as the control of pollution sources,the improvement of energy efficiency,use clean energy and implement advanced environmental standards plays a crucial role in improving the air quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".