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Comparison of road freight charging in Visegrad Group countries in the context of sustainable regional development

2019· article· cs· W2949741627 on OpenAlexaff
Tomáš Kučera, Nikola Viteková

Bibliographic record

Venuenot available
Typearticle
Languagecs
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsTransport Canada
Fundersnot available
KeywordsTollContext (archaeology)Transport engineeringToll roadRoad transportSustainable developmentBusinessPaymentSustainable transportRoad trafficTraffic managementRegional scienceSustainabilityEngineeringGeographyFinancePolitical science

Abstract

fetched live from OpenAlex

Road transport plays an important role in the social and economic development of the state and regions.On the other hand, road transport is a source of emissions, noise and vibration and causes health and safety risks to humans.Road freight charging is being introduced for reasons of road freight transport restrictions.The article aims to make a comparison of the road freight charging in the Visegrad Group countries in the context of sustainable regional development.The article uses a comparison method, which belongs to the category of logical scientific methods.The websites of road transport charging operators are the source of the data in the individual countries of the Visegrad Group countries.The results of the comparison of road freight charging in the Visegrad Group countries are given for each country, with particular emphasis on the legal framework, toll rates and toll payment method in the specific country. Key words road freight transport,

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.264
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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