Estimation of the Probability Distribution of Wave Velocity in Wood Poles
Bibliographic record
Abstract
Ultrasonic testing is a nondestructive technique commonly used for in situ condition assessment of wood poles. A transmitter and a receiver are used in a transillumination configuration to measure the first arrival of compressional waves (P-waves). The P-wave velocity is computed using the distance between the transducers and the travel time. The condition assessment of wood poles is inferred from the comparison of the measured wave velocity and a reference velocity that depends on the wood species. A wave velocity smaller than the reference value indicates a reduction in the strength of the wood. The elastic and mechanical properties of wood (elastic moduli, mass density, and Poisson’s ratios) are random variables that vary significantly for the same wood species; consequently, the P-wave velocity in wood poles is also a random variable. A better understanding of wave propagation in an orthotropic material, including the uncertainty in the mechanical properties of wood poles, is required to improve the reliability of ultrasonic tests. This paper presents a new methodology to evaluate the probability distribution of the P-wave velocity in wood poles. This methodology is founded on results from numerical simulations, laboratory tests, a simplified model of P-wave propagation in an infinite cylindrical orthotropic medium, and the consideration of the uncertainty in the mechanical properties of wood. The condition assessment of wood poles is improved by comparing the measured wave velocities Vp at different receiver locations and the corresponding probability distributions of the wave velocity Vp for sound wood poles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".