Ring debarking efficiency of frozen balsam fir logs is affected by the radial force but not by the log position on the stem
Bibliographic record
Abstract
The effect of the radial force applied by a ring debarker tip to frozen balsam fir (Abies balsamea (L.) Mill.) logs, obtained at three positions on the stem, was studied. A one-armed ring debarker prototype was built, which controlled the radial force, the rake angle, and cutting and feed speeds. Balsam fir logs at −19 °C were debarked at three levels of radial force. The rake angle, tip overlap, tip edge radius, and cutting and feed speeds were kept constant. Debarking quality was evaluated by two criteria: the proportion of bark remaining on log surfaces and the amount of wood fibres in bark residues. Log characteristics (dimensions, eccentricity, bark thickness, and knot features), bark–wood shear strength, and the basic densities of sapwood and bark were also measured. Results showed that the radial force had a significant effect on debarking quality. The proportion of bark remaining on log surfaces increased while the amount of wood fibres in bark residues decreased as radial force decreased. A radial force of 18.5 N·mm−1 is suggested for an optimal debarking quality. In contrast, log position on the stem did not affect the debarking quality indicators. Results also showed that the proportion of bark remaining on log surfaces increases as bark–wood shear strength and the proportion of knot surface increase, while the amount of wood fibres in bark residues increases as bark–wood shear strength decreases and logs are more eccentric. The results give useful information to improve the debarking process within the studied range of log diameters and debarking parameters.
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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".