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Record W2297068917 · doi:10.2495/sdp-v10-n5-701-712

Benefits and limitations toward a sustainable road environment during the years of economic recession

2015· article· en· W2297068917 on OpenAlexvenueno aff
Konstantinos Vogiatzis, Pantelis Kopelias

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2015
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRing roadTraffic volumeNoise (video)Traffic speedRecessionTraffic noiseRoad trafficEnvironmental scienceVolume (thermodynamics)Transport engineeringGeographyNoise reductionEngineeringEconomicsComputer science

Abstract

fetched live from OpenAlex

The prolonged economic crisis in Greece in the last 5 years resulted in a dramatic road traffic volume reduction and as a consequence in environmental road traffic noise diminution. This article analyses this issue with comparisons of measurements of noise, traffic volume and speed, at certain points along the capital's ring motorway, for a 9-year period. Data and comparisons concern measurements taken at 42 locations in Athens ring road during the last 9 years. According to the results, since 2009 -the year with the highest ring road traffic volume -there is a downward trend in both road traffic and environmental noise measurements recorded every year. It is also interesting to point out that until 2009, at locations where there is high traffic volume and many hours of congestion during the day, there is some minor reduction or even an increase in noise level due to the increase of the average speed. On the other hand, night measurements show that, for economy reasons, driver's speed has dropped to lower levels and thus lower noise level measurements are recorded.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.253

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 head0.052
GPT teacher head0.326
Teacher spread0.274 · 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 designQualitative
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

Citations7
Published2015
Admission routes1
Has abstractyes

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