Differential Etching of Rays at Wood Surfaces Exposed to an Oxygen Glow Discharge Plasma
Bibliographic record
Abstract
Basswood samples were exposed to oxygen glow-discharge plasmas for 30 min, and etching of radial and tangential longitudinal surfaces was measured. It was hypothesized that there would be a positive correlation between etching and plasma energy, and differential etching of wood surfaces because of variation in the microstructure and chemical composition of different woody tissues. Etching at the surface of basswood samples was examined using profilometry. Light and scanning electron microscopy were used to examine the microstructure of samples exposed to plasma. There was a large effect of plasma energy on etching of basswood surfaces, and radial surfaces were etched to a greater extent than tangential surfaces. However, rays at radial surfaces were more resistant to etching than fibers, resulting in greater variation in the etching of radial versus tangential surfaces. The same phenomenon occurred at radial surfaces of balsa wood, jelutong and New Zealand white pine subjected to plasma etching. The possible reasons for the greater resistance of rays to plasma etching are explored, and it is suggested that such differential etching of wood surfaces may impose a limitation on the use of plasma to precisely etch functional patterns at wood surfaces (raised pillars, grooves), as has been done with other materials.
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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 | 0.000 |
| 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".