Evaluation of Three Surfacing Methods on Paper Birch Wood in Relation to Water- and Solvent-Borne Coating Performance
Bibliographic record
Abstract
Helical planing, face milling, and sanding were used to surface paper birch wood prior to application of coatings.The surface roughness and wetting properties of the wood were evaluated as well as the pull-off strength of water-and solvent-borne coatings, before and after aging.The specimens surfaced with helical planing produced surfaces with the highest surface roughness, the best wetting properties, no subsurface damage, and good pull-off strength before aging.Those surfaced with face milling generated surfaces with intermediate surface roughness, lowest wetting properties, slight surface and subsurface damage, and good pull-off strength before aging.The sanded samples produced the lowest surface roughness, intermediate wetting properties, the highest surface and subsurface damage, and good pull-off strength before aging.After aging, all samples coated with the same varnish exhibited the same pull-off strength regardless of the surfacing treatment.However, loss in pull-off strength after aging was lower for helical planing than for the others.This suggests that helical planing could produce more suitable surfaces for indoor furniture applications.Finally, the water-borne coating created stronger bonds with the substrate than the solvent-borne coating.
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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 | 0.000 | 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".