Effects of cutterhead diameter and log infeed position on surface quality of black spruce cants produced by a chipper-canter.
Bibliographic record
Abstract
The effects of the cutterhead diameter and log infeed position on surface quality of black spruce (Picea mariana [Mill] B.S.P.) cants processed by a conical chipper-canter were evaluated. Three cutterhead diameters (345.2, 448.7, and 661.5 mm) combined with three infeed positions or vertical distance from the cutterhead axis to the bedplate on which the log was supported, were studied. The nominal linear cutting speed was fixed at 23.5 m/s. Rotation and feed speeds were adjusted to obtain a nominal feed per knife (chip length) of 25.4 mm. For each cutting condition, two sides of the log were machined at either frozen or unfrozen wood temperatures. Surface quality was analyzed according to waviness and roughness standard parameters. Results showed that surface quality was affected by the cutterhead diameter, infeed position, and wood condition (frozen and unfrozen). Surface quality improved as the vertical distance from the cutterhead axis to the bedplate increased. The global action of the bent knife induced some vibration into the canting edge, which could explain the variation in surface quality among infeed positions. Moreover, frozen logs produced smoother surfaces compared with unfrozen logs. In addition, the effect of the angle of the canting edge with respect to the wood grain on cant surface quality depended on the orientation of the growth rings and on the wood condition (frozen and unfrozen). These results give useful information to improve surface quality within the studied range of infeed positions and cutterhead diameters.
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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 | 0.001 |
| 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".