Full-scale fire study of spatial separation
Bibliographic record
Abstract
With rising land and infrastructure costs and the demand for affordable housing, there is increasing pressure to allow new houses be built closer together. A series of full-scale fire experiments were conducted to provide data to address spatial separation issues and measures to limit potential fire spread between adjacent houses. Wall assemblies with different exterior finishing (exterior gypsum board, aluminum siding on waferboard sheathing, or waferboard) were positioned at various wall-to-wall and eave-to-eave separation distances. The fire compartment contained a fuel package of wood mixed with ABS plastic pipes comprising a fuel load of 16.9 kg per square meter of the floor area. Flame issued from the fire compartmentthrough a rough opening on the exposing wall. Results showed the need for maintaining adequate spatial separation, including eave separation. Aluminum claddings on combustible sheathing or gypsum board as exterior sheathing, showed its effectiveness in reducing the danger of fire spread between houses built at a close distance and provided protection against fire spread for a period within the typical response time of a fire department. Blocking attic ventilation through eaves adjacent to neighbouring houses appeared to be another measure to reduce the fire spread potential.
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) | 0.001 | 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".