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Record W103161832

Optimizing Use of Reclaimed Asphalt Pavement in Flexible Pavements in Ontario

2009· article· en· W103161832 on OpenAlexaboutno aff
P Marks, C Cautillo, K K Tam, T Kazmierowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphalt pavementChristian ministryAsphaltEngineeringRutEnvironmentally friendlyTransport engineeringCivil engineeringWaste management
DOInot available

Abstract

fetched live from OpenAlex

In the Ministry's quest toward a more environmentally sustainable construction program, our recycling policies specifying when and how much Reclaimed Asphalt Pavement (RAP) is permitted in Hot Mix Asphalt (HMA) required review. The review came on the heels of the successful completion of a demonstration contract constructed in 1999 utilizing hot in-place recycling and Recycled Hot Mix (RHM). The demonstration showed that premium surface courses required for freeways could perform equally well with RAP in the HMA. To assist in revisiting our policy, a survey of other state agencies' recycling policies was conducted in 2008. Based on our findings, the Ministry is confident that the paving industry can produce HMA with higher percentages of RAP and has taken steps to permit HMA mix designers to make that choice. This also includes allowing RAP in mixes where we have traditionally not permitted any RAP, such as premium surface courses. Details of the Ministry's innovative pavement recycling program, which aims at providing a sustainable rehabilitation option that is safe, efficient, environmentally friendly, cost-effective, and that meets the needs of present-day users without compromising those of future generations, are summarized in this paper along with the results of our environmental scan.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.964

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 head0.056
GPT teacher head0.262
Teacher spread0.206 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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