Effects of log temperature, cutting width, and knots on the surface quality of the cants produced by a chipper-canter
Bibliographic record
Abstract
The effects of wood temperature, cutting width, and knots on the surface quality of black spruce cants processed by a chipper-canter were evaluated. Four matched groups of logs were machined at temperatures of 20°C, 0°C, −10°C, and −20°C. Each log was transformed at two cutting widths (CW: 12.7 and 25.4 mm). Knot characteristics were measured on the cant surfaces after log processing. Surface quality of cants was assessed by roughness and waviness parameters and torn grain. The quality of surfaces was affected by the temperature of logs and cutting width. Poorer surface quality was obtained at larger cutting widths. This was caused by increased cutting forces when processing more material at larger cutting widths, compounded by the presence of knots. Waviness and roughness were higher for frozen logs than for unfrozen logs. Although the sub-zero temperatures caused higher cutting forces and vibrations, their effect was partly offset by the strengthening of the earlywood and the brittle behaviour of frozen wood. Correlations and regression analyses showed that the optimisation of the cutting conditions for decreasing waviness and roughness should also reduce the torn grain depth. Moreover, the position and area of knots could be considered to minimise waviness and roughness and the occurrence 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) | 0.001 | 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".