Birefringence of wood at terahertz frequencies
Bibliographic record
Abstract
Fibre content of solid wood plays an important role in the wood products industry in terms of value. Additionally, fibre structure in composite wood products such as Oriented Strand Board (OSB) and paper products plays an important role in terms of strength properties. The effect of moisture content on wood properties is important in the manufacturing process and final product performance, and therefore its effect on the birefringence is of considerable interest. Since solid wood exhibits strong birefringence at terahertz frequencies, there may be potential applications of terahertz spectroscopy to fibre content and structure sensing. There are two potential sources for this strong birefringence: (i) form birefringence resulting from the porous structure of solid wood and (ii) intrinsic birefringence resulting from the dielectric properties of the material itself. In this report, the variability of birefringence within and between species, the dependence of the birefringence on moisture content and the relative contributions from form and intrinsic birefringence are examined. In order to clarify the role of these contributions to the measured birefringence, polarized terahertz reflection spectroscopy is examined and compared to the results obtained in a transmission geometry. Comparison of the birefringence measured in transmission and reflection geometries suggests that form birefringence may dominate.
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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) | 0.000 | 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) | 0.001 | 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".