MétaCan
Menu
Back to cohort
Record W2892600145 · doi:10.1139/cjce-2018-0044

Highway 400 precast concrete inlay panel project: Instrumentation plan, installation, and preliminary results

2018· article· en· W2892600145 on OpenAlexafffundvenueabout
Daniel Pickel, Susan Tighe, Warren Lee, Rico Fung

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsCanadian Electricity AssociationMinistry of Transportation of OntarioUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCement Association of Canada
KeywordsPrecast concreteInstrumentation (computer programming)TruckSlabEngineeringChristian ministryAsphalt concreteEnvironmental scienceCivil engineeringMoistureGeotechnical engineeringAsphaltStructural engineeringComputer scienceMaterials scienceAutomotive engineering

Abstract

fetched live from OpenAlex

The Ministry of Transportation of Ontario was interested in a rehabilitation strategy that could be used to address deep-seated rutting issues encountered on its 400-series highways. A precast concrete inlay panel (PCIP) rehabilitation design was developed and constructed involving the installation of precast panels into partially-milled asphalt pavement. Sub-surface instrumentation was installed at the PCIP–asphalt interface including earth pressure cells and moisture sensors installed in six instrumentation clusters. This instrumentation has been monitored to gather information regarding the PCIP trial installation. Readings from the moisture sensors indicate that water penetrates beneath the PCIPs in precipitation events, though these moisture levels recede under dry conditions, indicating that the water can exit the sub-slab area. Static load testing using a fully-loaded gravel truck was used to determine the different support reactions caused by different loading configurations. Higher loads were generally found beneath the joints in the two loading situations studied.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.686

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 communication0.0000.001
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 head0.024
GPT teacher head0.218
Teacher spread0.194 · 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

Citations3
Published2018
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207