EFFECTS OF LOG POSITION IN THE STEM AND CUTTING WIDTH ON SIZE DISTRIBUTION OF BLACK SPRUCE CHIPS PRODUCED BY A CHIPPER-CANTER
Bibliographic record
Abstract
Fifteen stems of black spruce (Picea mariana (Mill.) B.S.P.) coming from the Abitibi-Temiscamingue region, were cross-cut into three sections: bottom, middle, and top logs. Logs were fragmented producing three faces with a chipper-canter using three cutting widths (CW) of 12.7, 19.1, and 25.4 mm. Chip dimensions were assessed by thickness, width, and length (Domtar and Williams classifications). Knot characteristics [total knot number (TKN) and area (TKA)] were assessed in the three cant faces. Growth ring attributes [earlywood density, latewood density (LWD), ring density, earlywood proportion, ring width and rings per mm (R/mm)], mechanical properties (shear, splitting, modulus of elasticity (MOE) and modulus of rupture in bending), and basic density were evaluated on samples obtained within each CW area. The results showed that most of these wood attributes were affected by the log position in the stem and/or CW. The weighted mean chip thickness (WCT) and chip size distributions were significantly affected by the log position and CW. WCT increased as CW increased. WCT variation with height could be principally associated to the number and size of knots within the stem. However, the presence of higher taper in the bottom logs produced thicker chips. Multiple linear regressions showed that CW, TKN, LWD, and TKA were significant predictors of WCT. Moreover, chip thickness distribution was affected primarily by TKA, cutting height and LWD, while the width and length distribution was mainly affected by R/mm, TKN and MOE. Chip size variation is to some point determined by knot characteristics, bending properties, growth ring width, and wood density of the raw material. These results showed the potential benefits of classifying logs in woodyards and better controlling the raw material attributes in sawmills. If the CW is combined with the knowledge of the raw material, chip dimensions can be adjusted using other fragmentation parameters to increase chip size uniformity.
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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 | 0.000 |
| 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".