The Role Played by the Rake Angle of a Strander-Canter When Processing Jack Pine Logs
Bibliographic record
Abstract
The optimization of the machining parameters of strander-canting is the best way to obtain the optimum strand size, a better quality of the cant surface, and lower energy consumption. The effect of the rake angle on the performance of a strander-canter when processing jack pine logs was evaluated. Thirty-nine logs were cut with three rake angles (59°, 64°, and 69°). The counter-knife angle used in this study was 20° for frozen logs and 35° for unfrozen logs. The cutting speed and width were fixed at 25 m/s and 20 mm, respectively. The results showed that the rake angle affected the strand width, strand proportion, and energy requirements to transform the logs under frozen conditions. The rake angle of 64° produced a higher proportion and larger strands with less energy consumption than the rake angle of 59°. However, using a rake angle of 64° produced poorer surface quality. On the other hand, the effect of the rake angle on the processing of unfrozen logs was only noticeable when the rake angle changed to 69°. The proportion of pin chips increased, and the surface quality became poorer as the rake angle changed from 59° to 69°. The rake angle did not affect energy consumption when transforming the logs under unfrozen conditions.
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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 | 0.002 |
| 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 | 0.001 |
| 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".