Cutting speed and feed-per-knife effects on surface quality of cants produced by a chipper-canter
Bibliographic record
Abstract
The effects of cutting speed (CS) and feed-per-knife (FK) on the surface quality of black spruce (Picea mariana [Mill.] B.S.P.) cants processed by a chipper-canter were evaluated. Nine matched groups of logs were studied at 20, 25, and 30 m/s of CS, and 19, 25, and 32 mm of FK. Each log was processed at frozen and unfrozen conditions. Knots and grain angle measurements were taken on the cant surfaces after machining. The quality of cants was assessed utilizing waviness, roughness, and torn grain. The results showed that the surface quality was affected by CS and FK. Surface quality improved as FK decreased, likely due to decreasing cutting forces. The waviness tended to improve as CS increased, which could be partly due to the reduction of the non-cutting period between knives at higher CS. The waviness and depth of torn grain were similar for frozen and unfrozen logs. Surface quality varied within the cant, being generally poorer in the lower half. Knots and orientation of spiral grain (left-handed) contributed to diminishing the quality of surfaces. Finally, the results of correlations and regression analyses showed that optimizing the cutting conditions to decrease waviness should also reduce the depth of torn grain.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".