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Record W2034881748 · doi:10.1139/l08-101

Alternative single-number indicator of longitudinal road unevenness

2009· article· en· W2034881748 on OpenAlexvenueno aff
Oldřich Kropáč, Peter Múčka

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Roughness IndexTraverseWavinessComputer scienceIndex (typography)Performance indicatorTransport engineeringEngineeringSurface finishGeography

Abstract

fetched live from OpenAlex

For the full characterization of the longitudinal road unevenness based on the road elevation power spectral density (PSD), at least two independent indicators are necessary: the unevenness index and the waviness. Yet, for road management purposes, single-number unevenness indicators are still required and a number of such indicators have been proposed to date. The main problem of this issue consists in an adequate combination of the two mentioned indicators to obtain a single indicator whose application would fulfil the requirements, which are sometimes contradictory. The importance of the interaction coupling between the road and the travelling vehicle, in which the vehicle speed also plays a significant role, is emphasized. An alternative single-number indicator is proposed based on the equivalent vibration response effect, which the uneven road causes on the traversing vehicle. Other approaches to this problem are briefly discussed using comparative examples, including the indirect approach, a proposal for the modification of the international roughness index (IRI), and an assessment of the subjective rating methods.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.193
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations10
Published2009
Admission routes1
Has abstractyes

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