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Record W2418233820 · doi:10.15760/etd.2871

Metrication in highway design and operations

2000· report· en· W2418233820 on OpenAlexaboutno aff
Taher Al-Fadhli

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsTransport engineeringMetric (unit)Metric systemConstruct (python library)EarthworksConventionNotationProcess (computing)Agency (philosophy)EngineeringComputer scienceCivil engineeringEngineering managementOperations managementMathematicsPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

The complex problem of metrication of highway design, construction and maintenance has been the subject of a number of studies and pilot projects carried out by the Federal Highway Administration (F.H.W.A.) of the United States and the Roads and Transportation Association of Canada. Through a research grant, the FHWA of the U.S.A. assigned the Ohio Department of Transportation, the Illinois Department of Transportation and the Oregon Highway Division to design, construct, and maintain these metric projects. The author has reviewed the experience and conclusions of these studies and the problems encountered in the pilot projects relative to the design, survey, contracting, construction, signing, and public reactions to the metrication process. In the conclusions to this review, the author questions some of the recommendations of these studies, specifically in the area of curvature and station definition, notation convention, and the concept of dual signing. In these and other areas recommended guidelines for highway metrication are proposed. Finally, the author presents a set of equations and computer generated tables for highway design and layout in metric incorporating the flexibility of 10, 20, 40, and 100 metre station and curvature definition.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.355

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 head0.031
GPT teacher head0.238
Teacher spread0.206 · 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 designNot applicable
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

Citations0
Published2000
Admission routes1
Has abstractyes

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