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Record W4391167993 · doi:10.1080/09640568.2024.2303630

Can green concrete help address the sand and aggregate crisis? A scoping literature review

2024· article· en· W4391167993 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.

Bibliographic record

VenueJournal of Environmental Planning and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
FundersNational Marine Fisheries ServiceU.S. Geological Survey
KeywordsAggregate (composite)Green infrastructureBusinessEnvironmental scienceEnvironmental planningMaterials science

Abstract

fetched live from OpenAlex

Construction material industries, including the concrete sector, drive a huge demand for aggregates, including sand, one of the most widely consumed resources globally. Emerging advocacy campaigns on sand sustainability frame less aggregate intensive “ecological” or “green” concrete materials as solutions to mitigate the socio-environmental impacts emerging from sand consumption. This scoping literature review considers how the benefits from green concrete are portrayed in the construction material-centered academic literature. The scholarship reviewed highlights that conventional concrete materials generate environmental problems that green concrete products could help to mitigate, most notably CO2 emissions. Much less emphasis is placed on sand requirements, while the scholarship approaches sand sustainability very vaguely. We conclude that such caveats pose important challenges to the enactment of sounder sand policy. If the sand crisis is to be addressed, we advocate for the sand advocacy and green concrete epistemic communities to better align how they promote wider systemic change.

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)0.000
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score0.429

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 communication0.0000.000
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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