A Traceability Framework to Enable Circularity in the Built Environment
Bibliographic record
Abstract
The transition towards a Circular Economy (CE) has been receiving an increasing amount of attention in the built asset industry. One of the key aspects of CE is traceability, which can enable the identification and tracking of materials, products, and their associated value throughout their entire lifecycle. However, achieving traceability has been challenging in the built asset industry due to the complex nature of construction projects and a lack of awareness about the benefits of traceability in achieving the circularity of building products and materials. Despite recent studies and efforts, a limited number of frameworks or guidelines exist to support traceability in the built asset industry. In many cases, several of the existing traceability standards, strategies, and guidelines must be identified and framed to support development and implementation of theories and models applicable within the built asset domain. This paper proposes a traceability framework consisting of five key components covering: the main purposes of traceability enabling CE principles, the role of traceability across asset lifecycle stages, the type of data needed to support traceability, the value of collaboration and coordination among industry stakeholders, and key enablers and drivers of traceability from technological and organizational perspectives. The proposed framework developed in this paper contributes to the effort aimed at framing the knowledge domain of CE through the traceability of products and materials in the built environment.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".