Determination of log moisture content using ground penetrating radar (GPR). Part 1. Partial least squares (PLS) method
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Bibliographic record
Abstract
Abstract Ground penetrating radar (GPR) is a handheld system showing good potential for the real-time and nondestructive characterization of wood moisture content (MC). However, measurements performed over logs can be challenging because of their curved surface that can affect the GPR signal. In this study, the MC of thawed and frozen logs was estimated for three species (quaking aspen, balsam poplar, and black spruce) using the full GPR signals and the partial least squares (PLS) regression method. The signal was acquired from the cross-section (CS) and through the bark (TB) of the logs with and without an aluminum plate placed under the log. The full GPR signal does not provide better log MC prediction accuracy for small logs compared with the early-time GPR signal. The information about the shape and diameter of the log is contained in the direct and reflected waves of the GPR signal. CS models provided more accurate log MC prediction (RMSE v =7–25%) than TB models (RMSE v =6–40%) for the hardwood species. Thawed and frozen log models showed similar performances. This study demonstrates that GPR in combination with PLS regression is suitable for predicting log MC in the field.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it