Digitizing Wood | Analyzing Wood Grain in 2x4s using Facial Recognition Software Strategies
Bibliographic record
Abstract
Zippered Wood is a novel take on wood joinery and defor¬mation in which digitally generated formally specific joint patterns are cut into boards that are joined to produce pre¬dictably precise bends. Within this system research is being done to maximise the usability of the 2x4’s and the strength of the zippered wood. By using waste stream 2x4’s for zippered wood we inherit problems that are not common in their tradi¬tional use. Namely those are the knots and screw/nail holes in the wood. Once the pieces are milled these areas create weak points in the veneer. Through our testing we have found that these areas are prone to cracking during the gluing process and create potential failure points along the piece. In response to this we have begun to develop a simulation algorithm to strategically map the zipper tooth surface within the 2x4 to find the optimal placement in relation to any de¬fects identified in the grain of the 2x4. This process, Digitizing Wood, uses an image of the 2x4 to locate the knots and defects in the wood to determine the optimal location. Using standard facial recognition techniques and imaging processing algo¬rithms the knots and defects are marked. The marked regions and the zipper tooth surface are run through an evolutionary solver to optimize the placement in the 2x4. Once complete the finished piece is stronger and more materially efficient in its use. The Digitizing Wood strategy seeks to improve the effectiveness of the Zippered Wood system.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".