A Parameterized Method for Air-Quality Diagnosis and Its Applications
Bibliographic record
Abstract
A parameterized method is developed to diagnose the air quality in Beijing and other cities with an index termed (parameters linking air-quality to meteorological elements PLAM) derived from a correlation between PM10and relevant weather elements based on the data between 2000 and 2007. Key weather factors for diagnosing the air pollution intensity are identified and included in PLAM that include atmospheric condensation of water vapour, wet potential equivalent temperature, and wind velocity. It is found that the poor air quality days with elevated PM10are usually associated with higher PLAM values, featuring higher temperature, humidity, lower wind velocity, and higher stability compared to the averaged values in the same period. Both 24 h and 72 h forecasts provided useful services for the day of the opening ceremony of the Beijing Olympic Games and subsequent sport events. A correlation coefficient of 0.82 was achieved between the forecasts and (air pollution index API) and 0.59 between the forecasts and observed PM10, all reaching the significant level of 0.001, for the summer period. A correction factor was also introduced to enable the PLAM to diagnose the observed PM10concentrations all year round.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".