Analysis and Evaluation of the Effect of Studded Tyres on Road Pavement and Environment (II)
Bibliographic record
Abstract
In order to reduce a negative impact of studded tyres on the environment (i.e. emissions of fine (PM2.5) and coarse (PM10) particulate matter and noise) various scientific investigations are carried out to decide if the use of studded tyres shall be allowed or prohibited. This is especially topical for the countries belonging to the northern latitudes of the Earth‘s hemisphere where in winter months the air temperature drops below 0 °C, i.e. Scandinavia (Norway, Sweden, Finland), east and middle Europe (Lithuania, Latvia, Estonia, Germany, etc.), also USA, Japan, Canada and Russia. Based on the scientific investigations that the use of studded tyres causes emissions of pollutants, especially of PM2.5 and PM10 (Al, Si, K, S, Zn, W etc.), into the ambient air several tens of times higher than by the use of non-studded tyres and generates the increase in the noise emissions up to several tens of times, it could be stated that the use of studded tyres should not be obligatory. Thus, in “mild” Lithuanian winter when the air temperature often varies around 0 °C the use of studded tyres when travelling of icy and more rarely cleaned roads of Lithuania should be only recommended since the ice layer of the road pavement is effectively surmounted by the new generation winter tyres, i.e. less dangerous for the environment, manufactured from a more soft rubber mixture, containing a chemical element silicon.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".