Probabilistic characterization of ultrasonic wave propagation in wood poles
Bibliographic record
Abstract
Wood poles are widely used in North America to support power electric transmission and distribution lines. Wood poles are continuously exposed to wide ranging temperature and moisture conditions, making them vulnerable to internal decay and rotting. The resulting loss of strength makes the poles vulnerable to failure under adverse weather conditions, such as wind and snow storms. These failures can result in forced outages and customer disruptions with significant economic losses. Ultrasonic testing is a non-destructive method that has been used for detection of internal deterioration of in-service wood poles, which is based on the comparison of the measured wave velocity with a reference wave velocity associated with sound wood. The current ultrasonic methods assume that the reference wave velocity for a given wood species is constant in a pole cross section. This approach is simplistic because wood is an orthotropic material with highly variable material properties. This paper presents a method for probabilistic characterization of ultrasonic wave propagation in wood poles considering wood as an orthotropic material. A better understanding and characterization of ultrasonic wave propagation in a pole cross section will contribute to improve the condition assessment of in-service wood poles based on ultrasonic tests. As an example, P-wave velocity, surface waves, frequency response function, and magnitude spectrum area are used to characterize the propagation of ultrasonic waves at a cross section of a Douglas-fir pole of 25 cm diameter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".