An examination of property relationships in the Canadian machine stress rated lumber in-grade program
Bibliographic record
Abstract
The Canadian lumber industry undertook two large-scale test programs for the verification of lumber design properties of Canadian species combinations. The visual lumber in-grade test program was begun in 1983 (with a prior program undertaken in 1975), while a similar program for machine graded lumber (MSR lumber) was undertaken in 1988. Some of the results of the MSR lumber in-grade test program are examined in this thesis. Stiffness and strength results from the visual lumber in-grade program are used for comparison, as are values from the MSR lumber standard. The importance of differences in methods of testing properties, particularly Modulus of Elasticity (MOE), is shown. Differences in results occur due to changes in the test span to depth ratios, measurement techniques and location of defects. The importance of knots as a cause of failure in both bending and tension is examined. The high incidence of lumber failures initiating at points where no defect was visible to the human eye is also studied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".