Determination of log moisture content using ground penetrating radar (GPR). Part 2. Propagation velocity (PV) method
Bibliographic record
Abstract
Abstract Log moisture content (MC) has been determined based on the propagation velocity (PV) of ground penetrating radar (GPR) signals. This approach is based on measuring the travel time of the GPR signal through the log, from which its PV and the apparent log dielectric permittivity can be retrieved. Linear regression between the log dielectric permittivity and MC was established for each of the investigated wood species (quaking aspen, balsam poplar, and black spruce), log state (thawed and frozen), and direction of measurement [on the log cross-section (CS) and through the bark (TB)]. CS and TB measurements led to different results depending on the log state and wood species. Linear models with different slopes were found for thawed (slope=6.4–9.8) and frozen (slope=12–29) logs due to the difference in the dielectric properties of the frozen and unfrozen water in wood. The models for quaking aspen and balsam poplar were very similar to each other and differed from that of black spruce in terms of slopes and intercepts. Generally, the PV method leads to poorer log MC prediction accuracy than the partial least squares method presented in Part 1 of this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".