A Framework for Estimating the Reuse Value of In Situ Building Materials
Bibliographic record
Abstract
Rapid global population growth is leading to an increase in demand for raw materials. The construction sector is responsible for a major part of global material consumption, accounting for approximately 50% of raw material extraction. Although the demand for construction materials is increasing, natural resources are becoming scarcer. One solution to mitigate the pressure on natural resources is to implement urban mining strategies. These will require demolition, disassembly, and deconstruction processes that belong to the broader construction domain as it evolves toward a circular economy in the built environment. Significant work has been conducted on measuring and managing the availability of secondary materials in material banks. However, there is a lack of understanding of the market value of these material banks and their potential for decreasing raw material consumption. In this research, a framework that can be used to estimate the reuse and recycling market value of in situ materials is developed. The results of this research aim to positively impact the transition to a more circular economy in the built environment by bringing insight into the value of materials that are currently in-use but have the potential to support future demands.
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".