Wood property relationships and survival models in reliability
Bibliographic record
Abstract
We study the relationship between lumber strength properties and their visual grading characteristics. This topic is central to the analysis of the reliability of lumber products in that it underlies the calculation of structural design values. The approaches described in the paper are adaptations of survival analysis methods commonly used in medical studies. Because each piece of lumber can only be tested to destruction with one method (i.e., each piece cannot be broken twice), modeling these strengths distributions simultaneously can be challenging. In the past, this kind of problem has been solved by subjectively matching pieces of lumber, but the quality of this approach is then an issue. The objective of our analysis is to build a predictive model that relates the strength properties to the recorded characteristics. The paper concludes that type of wood defect (knot), a lumber grade status (off‐grade: yes/no), and a lumber's module of elasticity have statistically significant effects on wood strength. We find that the Weibull accelerated failure time model provides a better fit than the Cox proportional hazards model in our dataset. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".