Bibliographic record
Abstract
An optimized 24f glulam beam lay-up has been investigated with a series of laboratory testing and computer modeling. The basic ideas of these assessments are to increase the efficient use of timber resource in the glulam construction with integration of reliability based procedures to characterize the specified strengths for the glulam beams. During this study, existing grade specifications in the Canadian Standards have been refined. Five new Douglas fir lamina grades (T1, Cc, B, C and D) and their tensile strength data have been established. Finite element glulam analysis program ULAG has been used for the primary beam modeling and analysis. New routines to account for the laminating effects on the beam strength and to evaluate the shear capacity of the glulam beams have been incorporated in the ULAG program. The shear stress output from the finite element analysis has been integrated to consider weakest link stress volume effect for the shear capacity assessment. Subsequently 24f glulam beams have been successfully simulated using the refined ULAG program. A similar analysis based on ASTM D3737 has been carried out using the US-GAP program based on a detailed knot survey on the new lamina grades. The model has been calibrated by full scale test results. The model predictions and the corresponding assessments have been further validated by a second set of full scale glulam bending and shear tests. Glulam beams 305 mm and 610 mm deep, have been tested to assess the flexural strength. Three sets of glulam beams, 305 mm and 457 mm deep have been tested at short span to depth ratios to determine the shear capacity. Excellence prediction accuracy by ULAG has been confirmed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".