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Record W2316507768 · doi:10.1061/9780784412817.002

Performance Measures And Evaluation of Asphalt Pavements Using the Internal Roughness Index

2013· article· en· W2316507768 on OpenAlexaffabout
Ningyuan Li, Ren-Jie Qin, Zhaohui Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsInternational Roughness IndexAsphaltRide qualityIndex (typography)Asphalt pavementPavement managementMeasure (data warehouse)Computer scienceAsphalt concreteTransport engineeringEnvironmental scienceSurface finishCivil engineeringEngineeringAutomotive engineeringMechanical engineeringMaterials scienceData mining

Abstract

fetched live from OpenAlex

International Roughness Index (IRI) has become a well-recognised tool and standard measurement of pavement riding quality. The IRI is also used by many road agencies as an end-result specification for newly constructed asphalt concrete pavements. As the IRI is a geographically transferable, repeatable, auditable, and time-stable means, it has been largely considered as a measure suitable for evaluating pavement performance evaluation. This paper describes analysis of IRI measurements provided by several contractors using different IRI measuring devices conducted on 12 representative pavement sections in Canada. Furthermore, the influence of different devices on IRI measurement is carried out through statistical analysis.

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 score0.537
Threshold uncertainty score0.150

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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