METHODOLOGY FOR ASSESSING THE LEVEL OF ATMOSPHERIC POLLUTION BY ROAD TRANSPORT IN THE PROJECTS OF MANAGEMENT OF ENVIRONMENTAL STATE OF A CITY
Bibliographic record
Abstract
The level of environmental pollution is the main criterion that determines the quality of living conditions in cities. One of the most dangerous environmental problems of cities that affects the health of the population is atmospheric air pollution by road transport. In the city of Kyiv, the total volume of pollutant emissions from stationary sources in 2020 amounted to 25.5 thousand tons, from mobile sources almost 9 times more – 225.8 thousand tons. The comprehensive air pollution index (API) is used to characterize air quality in cities which allows you to determine how many times the total level of air pollution with several impurities exceeds the permissible value and to identify substances that contribute the most to atmospheric pollution. In most European countries, the USA, Canada and others, the air quality index (AQI) is used to control the level of atmospheric air pollution. When calculating the AQI, the concentration of pollutants is determined by field studies (monitoring) or mathematical modelling. In contrast to monitoring, which is a rather expensive study, mathematical modelling provides not only an operational assessment of the level of atmospheric pollution but also makes it possible to forecast the state of the air and to determine strategies for reducing pollutant emissions. In this regard, the creation of methods that allow making operational forecasts of the level of atmospheric pollution in cities and preventing critical situations in which the concentration of pollutants exceeds the maximum permissible values is an extremely urgent task.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".