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Record W2092376010 · doi:10.1139/l05-073

An evaluation of warranty contracting in the United States of America

2006· article· en· W2092376010 on OpenAlexvenueno aff
Mehmet Emre Bayraktar, Qingbin Cui, Makarand Hastak, Issam Minkarah

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
FundersOhio Department of Transportation
KeywordsWarrantyBiddingBusinessSuretyQuality (philosophy)State (computer science)Actuarial scienceProcess (computing)Operations managementFinanceEconomicsMarketingComputer scienceLaw

Abstract

fetched live from OpenAlex

It is believed that warranty contracting would benefit state highway agencies by improving quality, saving money and time, and encouraging contractors' innovations. However, the challenges associated with warranties could be substantial, including higher costs, early failures, a reduction or even elimination of small contractors from the bidding process, and an increase in contract disputes and litigation. The actual impact of warranty provisions on state Department of Transportation (DOT) projects in the United States of America is still unclear because of limited industry experience. This paper provides a detailed discussion of warranty provisions and the benefits and problems associated with the subject matter. The advantages and disadvantages of warranty provisions are evaluated from the conflicting perspectives of state Departments of Transportation (DOTs), contractors, and surety companies. The future prospects of warranty provisions are established based on the findings of a recent study of warranty provisions and existing literature. Also, several options are proposed that have a strong potential to improve the application of warranties on DOT projects.Key words: warranties, highway construction, innovation, highway maintenance, contract administration, construction costs, construction inspection.

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)0.001
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 score0.053
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.209
Teacher spread0.201 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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