Evaluation of Four Surfacing Methods on Black Spruce Wood in Relation to Poly(Vinyl Acetate) Gluing Performance
Bibliographic record
Abstract
Oblique cutting, peripheral planing, face milling, and sanding were used to surface black spruce wood prior to gluing with a two-component poly(vinyl acetate) adhesive. Surface roughness, anatomical features of surfaces, and glueline interfaces as well as the glueline shear strength before and after aging were evaluated. Oblique-cut surfaces presented no subsurface damage, little fibrillation, low roughness, thin gluelines, and little adhesive penetration. Peripheral-planed and face-milled surfaces both showed slight cell deformation and a higher level of fibrillation. The large number of cell lumens available and the fibrillation appeared to favor the penetration of adhesive as well as to increase surface roughness. Sanded surfaces were the smoothest, and their anatomical structures were the least visible of the four machining processes. These samples also showed more important subsurface damage, which limited the penetration of adhesive. For the glueline shear strength before and after weathering, no significant differences occurred among the surfacing treatments. The microscopic and topographic differences among the surfacing treatments were not sufficient to generate significant differences in glueline shear strength. Peripheral planing and face milling should be better alternatives with respect to productivity.
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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".