NO<sup>2</sup> Concentration Modelling Using Meteorological and Traffic Features.
Bibliographic record
Abstract
The constantly evolving urban areas also increase the concentration of emission gases.This fact has an adverse effect on habitants' health and life quality.A scientific basis, such as the development of accurate and adequate models of air pollution, is necessary to be able to influence decision-makers in such a way that real actions to improve air quality are carried out.Therefore, creating and improving models describing this phenomenon is extremely important.Both linear (Multiple Linear Regression) and non -linear methods (Random Forest) were used for modelling concentrations of pollutants in atmosphere.Based on the traffic, meteorological and pollution data from 2015 -2020 in Wrocław, it was shown that it is possible to predict concentration of NO2 with high accuracy: R 2 statistic reaches 88% while predicting daily average concentration using Random Forest methodology.Multiple Linear Regression provides worse fit and is biased by necessity to comply with its statistical restraints.Regardless of its costs it provides explicit interpretation of each factor in model.It was shown that there is a strong correlation between pollutants concentration and traffic volume as well as meteorological factors.Models perform significantly better when to the set of predictors concentrations of other pollutants are included.Modelling every natural phenomenon is a very challenging task.In order to excel, the further study in the area of models' optimalization, investigation relationships naturally occurring, and including new variables must be performed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".