Bibliographic record
Abstract
Abstract The author has developed a series of computer models to predict the fire resistance of wood‐framed walls and floors. The models utilize two‐dimensional heat‐conduction equations and thermo‐physical property data to describe heat transfer through the assemblies. The model for wall assemblies WALL2D, the basic version of the wall model, has already been published in Fire and Materials. Recently, WALL2D has been extended to WALL2DN to analyse heat transfer through insulated walls and walls that experience openings at the joints between adjacent sheets of gypsum board. Since gypsum board shrinks at high temperatures, the joints between adjacent sheets of gypsum board open. Hot fire gases, thereby, enter the openings and heat the edge of the gypsum board and wood studs. The new model WALL2DN simulates the joint opening and describes the resultant effect of openings. The model also calculates heat transfer through insulation in the stud cavity and depicts the effect of insulation on the fire resistance of non‐load bearing wall assemblies. Insulation selected in WALL2DN is glass‐fibre insulation, rock‐fibre insulation, polystyrene foam and polyurethane foam. When walls are exposed to fire, the insulation in the cavity shrinks (and/or melts) and an empty space appears at the interface between insulation and gypsum board. The model simulates this shrinking behaviour of insulation in the cavity. Finally, the model was validated by comparing the predicted results to those from full‐scale standard fire‐endurance tests. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".