The impact of sanding grit size on color consistency of different wood species used in Canadian furniture manufacturing : a visual inspection
Bibliographic record
Abstract
Wood finishing consists of several preparation steps to the aesthetic appearance of wood and also to protect it from damage. In the very first step of finishing, wood defects such as oil, imperfections, and indentations are removed from the surface using multiple grits of sandpaper. Once the surface has been sanded, stain and finish can be applied evenly to the wooden surface. The natural color and cellular structure of each wood determine the final result of finishing. Open-grain woods such as oak usually have large pores, they absorb stains very easily and they do not turn blotchy. On the other hand, woods with closed grain structure like cherry and maple have small and dense pores, they do not absorb stain easily and evenly, and they tend to turn blotchy no matter to what grit they are sanded with. Other influential factors on the final result of finishing are stain type and the final sanding grit. To achieve the best results, each wood should be sanded with proper grit size, and finished with appropriate stains and finishes. In this study, I investigate the impact of sanding grit size on the color consistency of finished wood. For this purpose, samples of seven hardwood and softwood species were sanded using 120, 150, and 180 grit sandpapers, the stain was applied to the sanded wood surfaces using two methods, spraying and wiping stains, and finally the wood was finished with lacquer. For each individual wood species, the combination of grit size and staining method that achieved the best color consistency is reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".