Structural response model for wood stud wall assemblies: user manual
Bibliographic record
Abstract
The Structural Model program is a numerical model that provides a structural analysis of load-bearing, gypsum-protected, wood-stud wall assemblies. Given specific input data, the program predicts the deflection, load-bearing capacity, and graphically displays the extent of charring of a wood stud. The user is able to view the data in both text and graphical form. One feature of this program is that the data from the Database of Wall-2D1, a heat transfer model developed by Forintek Canada Corp., can be loaded. The user can then perform an analysis based on temperatures imported from Wall-2D. In addition, temperature from experimental data can also be imported for analysis. The Structural Model User Manual is designed to familiarize the user with the program. The system requirements and installation procedures are explained, as well as the input requirements and the process for running tests. Each of the menu items (i.e. File, Help, Print, etc.) is described. The program includes an index that allows the user to look up topics for additional help. This manual is not meant to provide technical specifications nor details of the theory used in the model. These aspects are explained in other documents.2,3
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.159 | 0.058 |
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".