Identifying the strength of boards using mixed signals of MOE and X-ray image
Bibliographic record
Abstract
The most accurate way of identifying the strength of lumber requires destructive testing which is clearly not useful for production of lumber. An intelligent mechanics-based lumber grading system was developed to provide a better estimation of the strength of a board nondestructively. This system processed X-ray-extracted geometric features (of 1080 boards that eventually underwent destructive strength testing) by using physical model of Lumber based on finite element methods (FEM) to generate associated stress fields. The stress fields were then fed to a feature extracting processor, which produced one strength predicting feature. MOE profiles were processed separately and a feature based on the minimum point in the MOE averaged profile, which was cut 15% from each ends. Then, the two MOE and X-ray extracted features were combined (with 4 different algorithms) to a single feature to estimate the strength of the boards. Applying 4 different algorithms to a database of more than 1000 boards, the strength of boards is estimated and coefficient of determination of 0.6365, 0.6510, 0.6514, and 0.6545 are achieved for different algorithms respectively.
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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 | 0.000 | 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".