The impact of mechanical log surface damage on chip size uniformity during debranching and debarking<i>Eucalyptus</i>pulpwood logs using a single-grip harvester
Bibliographic record
Abstract
Mechanised harvesting operations are becoming more prevalent in South Africa with the realisation that motormanual and manual harvesting operations pose significant health and safety risks to workers. The damage inflicted by single-grip harvester feed rollers and delimbing knives on log surfaces during debranching and debarking eucalypts, may affect eventual chip quality. Chip quality influences pulp quality and recovery in the kraft pulping process. This study investigates the influence of two mechanised debranching and debarking treatments on Eucalyptus pulp logs (threeand five-feed roller passes along the stem surface) by feed rollers and delimbing knives on chip uniformity, size and purity. The two mechanised treatments to three log classes (base, middle and top logs) were compared with chips produced from manually debarked logs. Manually debarked logs produced significantly less undesirable-sized chips than both three and five-pass processed logs. The volume of undesirablesized chips produced during chipping also increased with decreasing log size. Manually debarked logs produced chips with significantly less bark than three-pass-processed logs (0.008% vs 0.062%), and five-pass-processed logs produced chips with significantly less bark than three-pass-processed logs (0.018% vs 0.062%). Middle logs also produced chips with significantly less bark than base logs (0.016% vs 0.056%), and top logs produced chips with significantly less bark than base logs (0.017% vs 0.056%). In all cases the bark content on logs was considerably less than the maximum of 1.0% generally specified by kraft pulp mills.
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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".