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Record W629362891

Current State of the Pavement Warranty in the United States and Canada

2012· article· en· W629362891 on OpenAlexaboutno aff
Amin El Gendy, Yan Qi, Feng Wang

Bibliographic record

VenueTransportation Research Board 91st Annual MeetingTransportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsWarrantyState (computer science)Agency (philosophy)BusinessEngineeringTransport engineeringForensic engineeringComputer sciencePolitical scienceSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Pavement warranties are implemented by many states as a way to enhance pavement performance, reduce agency costs, and preserve pavement construction. This paper discusses the results of an online questionnaire that was conducted by Jackson State University to review the recent state of practice of warranty specifications in the United States and Canada. From the 34 states that responded to the questionnaire, Florida, Illinois, Indiana, Louisiana, Mississippi, Pennsylvania, and Wisconsin from the United States and British Columbia and Nova Scotia from Canada have pavement warranties on projects. The rest of the states are not planning to adopt pavement warranties in the near future. Although the literature showed some warranty projects in Maine, Minnesota, and New Mexico, they currently do not have pavement warranties on new projects. The paper summarizes and updates pavement warranty information in the United States and Canada based on the states participating in the questionnaire. Although individual state departments of transportation (DOTs) developed their own warranty specifications, which varied in terms of warranty type, warranty period, warranty items, performance evaluation method, etc., similarities are found in the sequence of warranty procedures and components that make up the pavement warranty program.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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