Bibliographic record
Abstract
Insulating and air sealing are effective ways to reduce the carbon footprints of homes and other buildings, and rigid foam panels are a popular material for doing that work. But many of the panels have a little-known climate cost: the global warming potential (GWP) of the gas, or blowing agent, used to puff the foam. In the case of panels made of extruded polystyrene (XPS)—a closed-cell foam suitable for wet or dry conditions—that gas has been hydrofluorocarbon-134a (HFC-134a). HFC-134a is a good blowing agent , but it has a GWP of 1,430, meaning it traps 1,430 times as much heat as carbon dioxide does over 100 years. As regulations seeking to eliminate HFC-134a take effect this year in Canada and the US, panel makers including Owens Corning and DuPont are switching their blowing agents to blends containing hydrofluoroolefins (HFOs). Refrigeration is the largest use for HFCs and other fluorocarbon gases,
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".