Unbuilding: How Deconstruction is Saving the Planet by Giving Building Materials a Second Life
Bibliographic record
Abstract
In 2018, more than 600 million tons of construction and demolition (C&D) waste was generated in the United States, more than twice the volume of ordinary trash created by American households and businesses. Even though 80-95% of this concrete, asphalt, steel, wood, drywall, glass and brick can be reused, repurposed or recycled, a quarter of it – or 145 million tons in 2018 – ends up in landfills. Thirty million tons of wood alone – often sturdy, irreplaceable old-growth lumber – is being trashed every year. Now, a budding ‘build reuse’ movement, made up of environmentalists, architects, historic preservationists, city planners, green builders, and entrepreneurs, hopes to change this status quo through the practice of ‘deconstruction’ – the eco-alternative to mechanical building demolition – to unlock the wealth of materials literally inside the walls of our buildings and make them available for reuse. A handful of cities have adopted or are considering ordinances that make deconstruction mandatory, in order to divert salvageable buildings materials from the waste stream. Link to capstone project: http://dianaducroz.com/
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.024 |
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".