METHODOLOGY FOR ASSESSING THE LEVEL OF ATMOSPHERIC POLLUTION BY ROAD TRANSPORT IN THE PROJECTS OF MANAGEMENT OF ENVIRONMENTAL STATE OF A CITY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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