Estimating the Strength of Boards Using Mixed Signals of MOE and X-Ray Images
Bibliographic record
Abstract
The most accurate way of identifying the strength of lumber requires destructive testing, which is clearly not useful for the production of lumber. An intelligent mechanics-based lumber grading system was developed to nondestructively provide better estimation of the strength of a board. This system processed X-ray-extracted geometric features (of 1080 boards that eventually underwent destructive strength testing) by using a physical model of the lumber based on finite-element methods (FEMs) to generate associated stress fields. The stress fields were then fed to a feature-extracting processor, which produced one strength-predicting feature. The modulus of elasticity (MOE) profiles were separately processed, and another feature was extracted based on the minimum point in the MOE averaged profile, with 15% of the data cut from each end. Then, the two MOE and X-ray extracted features were combined (with four different algorithms) into a single feature to estimate the strength of the boards. By applying four different algorithms to a database of more than 1000 boards, to estimate the strength of the boards, coefficient of determinationr2values of 0.64, 0.65, 0.65, and 0.65 were achieved for the different algorithms, respectively. The results were improved by dividing the database into two sets (based on the dates that the two batches were delivered), andr2values of 0.69, 0.71, 0.71, and 0.71 were achieved for the different algorithms, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".