Implementation of a mechanics-based system for estimating the strength of a board
Bibliographic record
Abstract
The most accurate way of determining the strength of lumber involves destructive testing. An intelligent mechanics-based lumber grading system was developed to provide a better non-destructive estimation of the strength of a board. This system processed the X-ray-extracted geometric features (of 60 boards that eventually underwent destructive strength testing) by using Finite Element Methods (FEM) to generate associated stress fields. The stress fields were then fed to a feature-extracting-processor which produced several strength predicting features. The best strength predicting features are determined by calculating the coefficient of determination (r/sup 2/) between the predicted and the actual strength of the board. Twenty six strength predicting features were generated by the processor. The estimated strength from each feature and from the combination of several features, was calculated and compared with the actual strength of the board. A coefficient of determination (r/sup 2/) of 0.43 was achieved by using the longitudinal (to the grain angle) maximum stress concentration (MSC).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".