Influence of Local Density on Concentrated Static Load Performance of Oriented Strandboard
Bibliographic record
Abstract
Abstract Fourteen 1,220 by 2,440 by 11.1-mm commercial Oriented Strandboard (OSB) panels were X-ray scanned to obtain horizontal density matrices. Localized densities around the concentrated static load (CSL) testing points of the panels were calculated prior to the CSL test. A linear regression analysis was conducted to assess the impact of the localized density on CSL performance. The results indicated that both deflection and ultimate load were highly correlated with the local density. Deflection and ultimate load were somewhat correlated (R2 = 0.52). The CSL deflection decreased and ultimate load increased significantly with increasing local density. The impact of local density on ultimate load was greater than on deflection. Horizontal density variation is inherent in OSB manufacturing processes, especially in the mat forming process. A number of factors, including evenness of strands in the metering bin, condition of picker rolls and dissolving rolls, and strand and fines surging, can affect horizontal density distribution. OSB panels with a high degree of variation in horizontal panel density may cause low density spots that increase the chance of failure in CSL test. It is therefore crucial to minimize the occurrence of very low density areas in order to reduce the odds of ultimate load failure. Reducing density variability allows OSB companies to increase the CSL properties of their products, which would otherwise need to be done by making the panel denser. Improving horizontal density uniformity allows for lowering of the average panel density, which reduces the manufacturing cost and helps improve the company's bottom line.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".