Development of a Life Cycle Inventory Dataset for Recycled ConcreteAggregates in the City of Abu Dhabi
Bibliographic record
Abstract
Environmental life cycle inventory (LCI) datasets are critical to conduct life cycle assessment (LCA) of concrete products.Obtaining these values from local practice is necessary to provide accurate assessment results that reflect real-life scenarios.The datasets for the recycling process of construction and demolition waste into recycled concrete aggregates (RCA) in the city of Abu Dhabi, United Arab Emirates are currently unavailable.Accordingly, this research aims to determine a detailed environmental LCI dataset for the production of RCA in Abu Dhabi.An extensive investigation of the RCA production practice was performed to highlight the input and output of each process unit.The methodology proposed by the International Standards Organization (ISO) to build an LCI (ISO 14040) was adopted.The resulting environmental LCI value for RCA production was determined to be 6.67x10 -4 kg CO2eq per kilogram of produced RCA.Research findings serve as a benchmark to evaluate the environmental sustainability of RCA and RCA-based products in a holistic LCA study, while also enriching the LCI of the city of Abu Dhabi.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".