Experimental Study of Optical Scattering and Fiber Orientation Determination of Softwood and Hardwood with Different Surface Finishes
Bibliographic record
Abstract
Optical scattering from wood of different surface finishes has been experimentally investigated. It has been found that for planed-Hemlock samples the scattering intensity distribution in the direction across the wood fibers is very close to perfect diffuse reflection, while that in the direction along the fibers appears as the combination of diffuse and specular reflection. The increase of wood surface roughness reduces the difference between these two scattering intensity distributions. The difference provides the basis for determining wood fiber orientation by measuring the scattering intensity variation with fiber orientation. When the sample surface becomes very rough, such as a rough sawn or porous hardwood sample, the scattering from the uneven surface overwhelms the difference and creates a difficulty in fiber orientation measurement. To solve the problem, we have been employed optical polarization detection in the investigation. With this detection method, for the samples used in this work, the intensity variation of the scattering vs. fiber orientation appears periodic despite the different surface finishes. With the help of Fourier analysis, fiber orientations of the rough sawn softwood and porous hardwood samples under investigation can be precisely measured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".