MétaCan
Menu
Back to cohort
Record W4245177100 · doi:10.1177/0361198106195800106

Correlating Chip Seal Performance and Construction Methods

2006· article· en· W4245177100 on OpenAlexaboutno aff
Douglas D. Gransberg

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2006
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsSeal (emblem)TraverseChipRoad constructionTransport engineeringEngineeringForensic engineeringOperations managementGeographyTelecommunicationsArchaeologyCartography

Abstract

fetched live from OpenAlex

A survey of U.S. public highway and road agencies that use chip seals as a part of their roadway maintenance program was developed and conducted as a part of NCHRP Synthesis of Highway Practice 342. Ninety-two individual responses from across the United States, Canada, and overseas were received. This paper seeks to correlate individual chip seal performance ratings with the construction practices reported to achieve those ratings. It finds a number of strong correlations. The most important is that the ambient air temperature specification is generally higher [average of 60°F (15°C)] for those respondents reporting excellent or good chip seal performance. The same trend was observed with the average amount of time before full-speed traffic was allowed to traverse a newly sealed road, with the best performing seals having an average wait period 28 h. Finally, respondents reporting the best performing seals used more preseal preparation measures and more detailed traffic control measures. The same respondents validated the success of their construction procedures by not requiring a fog or scrub seal to be placed on a freshly chip sealed road.

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)0.006
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 score0.137
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.068
GPT teacher head0.386
Teacher spread0.318 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207