Computational Modeling of the Lateral Load Transfer Capacity of Rimboard
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper evaluates the performance of structural composite lumber in terms of lateral load transfer capacity in rimboard application from the perspective of computational modeling with the finite element method (FEM). Computational modeling is effective at predicting and evaluating performance of structural composite lumber as rimboard. It provides insight into the system behavior of floor assembly and demonstrates that with the current test setup, as stipulated in the AC124 standard, the lateral load transfer capacity of rimboard may be overestimated if the vertical restraints in real buildings are not as stiff as the hold-down device used in the laboratory. Two solutions are proposed to improve the test setup to yield more reliable evaluation of lateral load transfer capacity of rimboard.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it