EFFECT OF KNIFE WEAR ON SURFACE QUALITY OF BLACK SPRUCE CANTS PRODUCED BY A CHIPPER-CANTER
Bibliographic record
Abstract
Effect of knife wear on surface quality of black spruce (Picea mariana (Mill) B.S.P.) cants machined by a chipper-canter was evaluated. A set of eight canting knives with six levels of edge recession (207, 290, 349, 449, 519, and 549 mm) was studied. Logs were fed at 145 m/min through the canter headrotating at 726 rpm yielding a nominal feed per knife of 25 mm. For each edge recession, two sides of the logs were machined at either unfrozen (above 14C) or frozen (below 23C) wood temperatures. Laserscanned profiles across the grain of 16 knife marks on each cant were evaluated for roughness and waviness parameters and depth of torn grain. The results showed that, regardless of log temperature, waviness and roughness were positively affected by edge recession. Roughness was more sensitive than waviness to changes in edge recession. Surfaces in general were smoother in frozen logs than in unfrozen logs. Maximum depth of torn grain appeared to not be significantly affected by knife wear. The results provided useful information for improving the performance of the chipper-canter in terms of surface quality.
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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 | 0.001 | 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".