Acoustic Testing and Sorting of Chinese Poplar Logs for Structural LVL Products
Bibliographic record
Abstract
The purpose of this study was to investigate the feasibility of using resonance-based acoustic technologies for sorting Chinese poplar logs for laminated veneer lumber (LVL) products. Representative poplar logs were sampled. Each log was first tested for acoustic velocity and then peeled into veneer. Each veneer sheet was subsequently dried and measured with a production-line veneer tester. LVL beams were made, and their stiffness was non-destructively measured by both time-of-flight (TOF) acoustic method and free-beam vibration methods. Based on the LVL dynamic modulus of elasticity (MOE) values, logs were sorted into several grades with known grade outturns. The results showed that there was a strong correlation between resonance-based acoustic velocities of logs and dynamic MOE of veneer and LVL. Thus, it is feasible to predict the stiffness of LVL products based on log resonance-based acoustic velocity measured. The resonance-based acoustic measurement is easy to use and reliable, which can help increase the grade outturn and in turn value recovery of Chinese poplar logs. It was estimated that the log grade outturns were approximately 31.1% for LVL grade 1, 38.6% for LVL grade 2, and 26.1% for LVL grade 3.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".