Effects of cutting speed and feed per knife on energy requirements for processing black spruce logs with a chipper-canter
Bibliographic record
Abstract
The effects of cutting speed (CS), feed per knife (FK), and temperature condition on energy requirements for processing black spruce logs by a chipper-canter were assessed. Nine groups of 15 logs were tested 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. Mean power increased as CS and FK increased. This behavior is explained by the mechanical relationships between the parameters and the rotation and feed speeds, as well as by the increase in the volume of wood cut. The energy consumption and the specific energy consumption increased as CS increased and FK decreased. For the three electrical criteria, more energy was consumed when processing frozen logs, which is due to the greater mechanical properties of wood. A positive relationship was identified between sapwood and heartwood moisture content, basic density, grain angle, and wood volume transformed into chips, as covariates, and the three energy criteria. These results give useful information on energy requirements to adjust cutting parameters of chipper-canters for a better energy management in sawmills.
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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".