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Record W561806643

Cement and Concrete Industries Contribution to Climate Change Mitigation

2009· article· en· W561806643 on OpenAlexaboutno aff
Timothy F. Smith

Bibliographic record

Venue2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsCementClimate changeTruckSustainabilityGlobal warmingEnvironmental scienceCivil engineeringEngineeringBusiness
DOInot available

Abstract

fetched live from OpenAlex

The Cement Industry is continuously trying to find ways to reduce its environmental foot print through development of a comprehensive strategy for reducing emissions and energy use. Another way the Cement Industry can assist with climate change mitigation is to encourage the use of concrete based products in Canada's Infrastructure. This paper gives a brief overview on the Canadian Cement and Concrete Industry and how cement is made. In addition, a brief introduction is provided on Canada's Cement Sustainability Initiative (CSI) which identifies an action plan addressing performance related to six key issues. The main focus of the paper is to identifying the Cement Industry's climate change mitigation initiatives and several concrete based applications that help mitigate climate change. Details are provided on research showing the advantages of concrete such as the updated Athena study looking at pavement structures' energy use and global warming potential, NRC's truck fuel usage studies and research on concrete as a CO2 sink. Other topics such as optimized concrete mixes and ultra high strength concrete are also discussed. An example of the potential fuel savings and associated CO2, NOx and SO2 reductions when operating on concrete pavement is also provided.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.977

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 head0.015
GPT teacher head0.221
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 designObservational
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

Explore more

Same venue2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGESame topicSmart Materials for ConstructionFrench-language works237,207