Effects of cutting speed and feed per knife on size distribution of pulp chips produced by a chipper-canter from frozen and unfrozen logs
Bibliographic record
Abstract
The cutting speed (CS) and feed per knife (FK) are among the most important variables affecting chip size produced by chipper-canters. Nine groups of black spruce logs were processed at three CS (20, 25, and 30 m/s) and three FK (19, 25, and 32 mm). Each log was processed under frozen (−13°C) and unfrozen (19°C) conditions. Chip size was assessed by thickness and by width/length. Chip size increased as CS decreased and FK increased. Frozen logs produced thinner chips and higher proportions of small chips. The weighted mean chip thickness (WCT) increased as the FK increased and CS decreased. The highest accepts proportion by thickness was obtained at 19 mm FK and 20 m/s CS, while the highest width/length accepts were produced at 32 mm FK and 20 m/s CS. Grain angle and knot proportion were the most significative covariates for chip size. Regressions showed that FK, CS, knot proportion, grain angle, and taper were the best predictors for WCT, explaining 86% and 81% of the WCT variations for frozen and unfrozen logs, respectively. Therefore, a combined evaluation of cutting parameters and raw material is essential to predict WTC, reduce chip size variations, and thus improve chip 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".