Analysis of PM<sub>10</sub> air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix
Why this work is in the frame
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Bibliographic record
Abstract
Abstract An analysis of air pollution by suspended particulate matter (PM 10 ) in Brno, the second largest urban agglomeration of the Czech Republic, based on generalized linear model (GLM) is presented. Average daily concentrations coming from PM 10 monitoring for the period 1998–2005 have been processed. The measured meteorological factors: air temperature and humidity, direction and wind speed were considered as covariates along with some additional seasonal factors. Three standard and six GLMs with strongly rank‐deficient design matrix have been applied. The rank deficiency is due to overparameterization which allows one more precise modeling involving, among others, identification of significant air pollution sources (PSs). From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on a four‐step modification of the basis pursuit algorithm originally suggested for time‐scale analysis of digital signals. As the standard estimation algorithms often fail due to numerical instability caused by strong overparameterization, we have applied this new computationally intensive approach allowing us to reliably identify nearly zero parameters in the model and thus to find numerically stable sparse solutions. The goal of the analysis was to identify the model and algorithm yielding most precise 1‐day forecasts of the level of pollution by PM 10 with regard to the meteorological and seasonal covariates. Copyright © 2009 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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 it