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Record W2121553503 · doi:10.5555/1030453.1030713

CEPM 4: optimization of the waste management for construction projects using simulation

2002· article· en· W2121553503 on OpenAlexaff
Mala Chandrakanthi, Patrick Hettiaratchi, Bolívar Prado, Janaka Y. Ruwanpura

Bibliographic record

VenueWinter Simulation Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConstruction wasteResource recoverySortingWaste managementWork (physics)Resource (disambiguation)Component (thermodynamics)Construction industryCleaner productionWaste recyclingEngineeringMunicipal solid wasteConstruction engineeringComputer scienceWastewater

Abstract

fetched live from OpenAlex

Growth in construction activities increases the amount of construction waste generated. Recycling of construction waste is an important component of environmentally responsible construction, as it reduces the amount of waste directed to landfills. In addition, it enhances the resource recovery for future construction work. A model is presented in this paper to predict waste generation rates, as well as to determine the economic advantages of recycling at construction sites. A future advanced version of the model can be applied to any construction site to: determine the amount of daily waste generation, resource and time requirement for sorting and transporting of recyclables. The model, therefore, is a valuable tool for construction managers interested in asserting the viability of recycling projects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.047
GPT teacher head0.250
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 source (direct Gemma or distilled Codex), 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

Citations25
Published2002
Admission routes1
Has abstractyes

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