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Record W2010931069 · doi:10.3141/2084-11

Sustainable Pavements

2008· article· en· W2010931069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsAsphaltChristian ministryEngineeringAsphalt pavementCivil engineeringSustainable developmentForensic engineeringWaste managementTransport engineering

Abstract

fetched live from OpenAlex

The Ministry of Transportation Ontario, Canada, is committed to using technologies to help build a more sustainable transportation system that supports today's needs while protecting the environment for future generations. Cold in-place recycling (CIR) is an established pavement rehabilitation technology that processes an existing asphalt pavement, sizes it, mixes in additional asphalt cement, and lays it back down without off-site hauling and processing. The added asphalt cement is typically emulsified asphalt. A recent development in CIR technology is the use of expanded (foamed) asphalt, rather than emulsified asphalt, to bind the mix. This combination of CIR and expanded asphalt technologies is termed cold in-place recycled expanded asphalt mix (CIREAM). Both CIR and CIREAM technologies support the philosophy of a sustainable transportation system. More specifically, CIR and CIREAM meet the criteria for a sustainable pavement: safe, efficient, economic, environmentally friendly pavement that meets the needs of present-day users without compromising those of future generations.

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.

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 score0.161
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.367
Teacher spread0.272 · 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