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Record W2341791497 · doi:10.14288/1.0103555

Increasing recycled material use : exploring the economic benefits of using more recycled concrete aggregate and recycled asphalt pavement in construction and rehabilitation projects in Metro Vancouver

2015· article· en· W2341791497 on OpenAlexaboutno aff
von Rautenkranz

Bibliographic record

VenueOpen Collections · 2015
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAggregate (composite)AsphaltAsphalt concreteAsphalt pavementWaste managementCivil engineeringForensic engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

One of Metro Vancouver’s goals is to increase the amount of recycled materials used in their construction and rehabilitation projects while being economically sustainable, preferably cost saving. The aim is to divert more recyclable materials from landfills, and to reduce stockpiles in recycling facilities. This research paper aims to explore examples from around the world that can be adopted by Metro Vancouver for two recyclable materials in particular: Recycled Asphalt Pavement (RAP) and Recycled Concrete Aggregate (RCA). This report looks at the current trends in recycling materials within Metro Vancouver acknowledging the barriers associated to their use in regards to a previous undergraduate assessment done on the topic (Ammerlaan). Further into the paper there is more detail on RAP and RCA which explores their uses and success stories in different parts of the world. Once an understanding of the materials has been established, some recommendations are put forward to Metro Vancouver for consideration.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.043
GPT teacher head0.246
Teacher spread0.203 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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