Impact of three alternative surfacing processes on weathering performance of an exterior water-based coating.
Bibliographic record
Abstract
Oblique cutting, face milling, and helical planing were used to surface black spruce wood prior to the application of an exterior acrylic water-based coating. Surface characteristics were assessed using an environment scanning electron microscope and coating performance was evaluated through pull-off measurements before and after a 3-year natural weathering exposure. Microscopically, oblique-cut surfaces were smooth with plateau-like areas, had a low level of fibrillation and few open lumens. Face-milled surfaces were characterized by a high level of fibrillation and numerous open lumens that favor coating spreading and penetration. Helical-planed specimens had an intermediate level of fibrillation and number of open lumens. After coating application, oblique-cut and helical-planed surfaces presented similar overall visual quality, whereas face-milled samples had an irregular appearance that degraded their quality. As a result, the latter were subjected to erosion during weathering exposure which further degraded their overall quality as well as pull-off strength. More specifically, face-milled samples had a significant inferior pull-off strength both before and after weathering. Oblique-cut specimens yielded higher initial pull-off strength but were associated with higher adhesion loss. According to the results, helical planing reduces adhesion loss of the coating studied over a 3-year exposure to yield a superior pull-off strength after weathering. Therefore, surfaces having a certain level of fibrillation still firmly attached to the surface and open lumens are desirable to increase mechanical anchorage of coating on black spruce wood surfaces.
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) | 0.001 | 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".