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Analysis and Evaluation of the Effect of Studded Tyres on Road Pavement and Environment (II)

2009· article· en· W2068131947 on OpenAlexaboutno aff
Rasa Vaiškūnaitė, Alfredas Laurinavičius, D. Miškinis

Bibliographic record

VenueThe Baltic Journal of Road and Bridge Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceParticulatesNorthern HemisphereEnvironmental engineeringEnvironmental protectionPhysical geographyGeographyAtmospheric sciencesGeology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.210
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2009
Admission routes1
Has abstractyes

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