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Record W2915513037 · doi:10.1080/10298436.2014.960998

An overview of various new road profile quality evaluation criteria: part 2

2014· article· en· W2915513037 on OpenAlex
Louis Gagnon, Guy Doré, Marc J. Richard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Pavement Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFuel efficiencyTruckTrailerAutomotive engineeringSuspension (topology)International Roughness IndexPoint (geometry)Computer scienceRadiator (engine cooling)Quality (philosophy)Environmental scienceTransport engineeringSurface finishEngineeringMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

This is the second part of an article which correlates road-induced impacts on vehicle to a selection of road assessment criteria. The impacts on tyre, suspension and radiator wear are studied by running a multibody semi-trailer truck model on 270 road profiles. The model accurateness is assessed by comparing international roughness index (IRI)–impact relationships to those published in the literature. A new profile rating method uses wavelength content to predict the impacts of a specific profile on driver and passenger health and safety, truck wear and fuel consumption. It is concluded that (1) medium wavelengths severely impact fuel consumption, component wear and safety; (2) simple, two-point and four-point indices yield similar results, but the more the points the better the correlation; (3) the IRI is good at predicting general trends in road-induced vehicular impact but is weak for specific impacts and (4) tyre wear correlates linearly while component wear requires quadratic correlations.

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.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.380
Teacher spread0.307 · 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