The Effect of the State of the Indoor Environment on the Air Quality inthe Cabin
Bibliographic record
Abstract
Air quality affects the state of the environment, human health as well as individual ecosystems to a significant extent.The permissible level of air pollution is determined by the national laws of each country and the EU.Air quality problems are related to the amount of pollutant emissions that escape into the atmosphere.These emissions have different origins.Transport is identified as one of the serious problems of the future also in terms of air pollution, especially in view of the rising trend of final energy consumption in road transport.One of the main factors of deteriorating air quality in the urban environment is burdened by high traffic density.Regulation of emissions from transport, including passenger cars, is in the general interest of European legislation.Transport produces almost a quarter of Europe's greenhouse gas emissions and is a major cause of urban air pollution.At the same time, it is necessary to deal with air quality in cars, which is affected by several factors.The aim of this article is to analyse the air quality in the cabin of a car that uses a pollen filter with activated carbon.The analysis is focused on particles from 0.3 to 10μm in 16 size categories that affect the health of people in the car.The influence of the fan setting and the state of the indoor environment on the particle concentrations will also be evaluated.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".