Open Construction: Envisioning a network for construction circularity in an urbanising landscape in the province of South Holland
Bibliographic record
Abstract
A nationwide program for building one million dwellings aims to relieve the Netherland’s housing crisis: nearly a quarter of this construction will take place in South Holland. Currently, the construction industry needs a huge input of raw materials that is not only causing waste problems but is also decreasing environmental quality. A large part of construction and demolition waste (CDW) is being downcycled, losing economic and material value. This creates not only a need but an opportunity for a construction and demolition (C&D) industry based on circular flows and biobased materials. The goal of this project is to produce a vision with strategies for the implementation of circularity along with the resolution of spatial conflicts in different scales.An overview of the spatial, technical and economic needs of the C&D industry and its externalities in urban environments was made. This resulted in the understanding of the spatial conflicts currently taking place between these two spheres of development and the potentials that circularity will have on jobs and consumption patterns. From this, a proposal for a circularity model with three components was formulated: an open network with a central production hub and peripheric logistic hubs, an open program for these hubs that adapts to current and future needs, and open edges that create interactions with their built and social 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".