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Record W44244910

National Cooperative Highway Research Program, NCHRP Synthesis 463, Pavement Patching Practices - A Synthesis of Highway Practice

2014· article· en· W44244910 on OpenAlexaboutno aff
RS McDaniel, Jan Olek, B. Magee, Ali Behnood, Richard L. Pollock

Bibliographic record

VenueUlster University Research Portal (Ulster University) · 2014
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersFederal Highway AdministrationTexas Department of TransportationMinnesota Department of TransportationCalifornia Department of TransportationAustralian GovernmentU.S. Department of Transportation
KeywordsState highwayHighway maintenanceTransport engineeringHighway systemRoad surfaceHighway engineeringEngineeringState (computer science)AsphaltControl (management)Best practiceRoad constructionCivil engineeringForensic engineeringPolitical scienceGeographyComputer scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This report summarizes current practices for patching both concrete and asphalt pavements. The intent is to document the state of the practice for patching relatively small-scale surface defects in concrete and asphalt pavements. Both reactive and planned patching is addressed. The synthesis covers management or administrative issues, materials, methods, equipment, specifications and tests, traffic control, and other aspects of patching operations.The information presented in the report was collected through extensive literature reviewsof U.S. and international sources. A total of 49 of 51 survey responses were received fromU.S. state highway agencies, a 96.1% response rate. Responses were also obtained from20 local agencies across the United States, 36 from national, county, and city agencies, andthree from maintenance contractors. Five responses were received from Canadian agencies (three provincial and two cities).The report will be of special interest to state, local, and international highway agenciesby assisting them to make informed decisions.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0010.001
Research integrity0.0000.001
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 head0.079
GPT teacher head0.348
Teacher spread0.268 · 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.

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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

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