Reuse and recycling of CDW materials and structures in energy efficient prefabricated elements for building refurbishment and construction - RE4
Bibliographic record
Abstract
The EU Waste Framework Directive 2008/98/EC states that all member states should take all necessary measures in order to achieve at least 70% re-use, recycling or other recovery of non-hazardous Construction and Demolition Waste (CDW) by 2020. In response, the Horizon 2020 RE4 project consortium (REuse and REcycling of CDW materials and structures in energy efficient pREfabricated elements for building REfurbishment and construction) consisting of 12 research and industrial partners across Europe, plus a research partner from Taiwan, was set up. For its success, the approach of the project was manifold, developing sorting technologies to first improve upon the quality of recycled aggregate. Simultaneously, CDW streams were assessed for quality and novel applications developed for timber, aggregate, and plastic waste in a variety of products including structural (e.g. beams, columns, slabs) and non-structural (e.g. building blocks, insulation panels, tiles) elements. With all products considered, innovative building concepts have been designed in a bid to improve future recycling of the products by promoting prefabricated construction methods and modular design to ease future recycling and increase value of the construction industry. The developed technologies and products will be put to the test in different test sites in building a 2 storey house containing at least 65% of CDW in the next 6 to 12 months.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".