Investigation of a novel insulation foam made from gypsum drywall waste
Bibliographic record
Abstract
Foamed plastic rigid insulation panels are effective for reducing building heating and cooling loads, with consequent reductions in energy use and associated greenhouse gas (GHG) emissions. However, plastic foam manufacturing and in-service use result in significant GHG emissions. In addition, plastic foams are flammable, and have been implicated in recent building fires. This paper describes proprietary mixtures and methods for producing drywall waste foam (DWF) panels, a carbon-neutral, fire-protective insulation made from gypsum drywall waste and other construction and demolition (C&D) waste materials. Gypsum drywall waste is inherently fire-protective and has relatively low thermal conductivity, but is a low-value commodity with few current reuse and recycling applications. DWF panels address two pressing issues in the built environment: decarbonization of building materials, and diversion of problematic C&D waste from landfills. Investigation of DWF panel engineering properties, including density, hardness, friability, burn-through time, and thermal conductivity are reported, with results compared to analogous commercially-available materials. Potential applications and areas for future investigation are also discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".