The impact of log surface damage caused by harvester<i>Eucalyptus</i>debarking on pulp value recovery
Bibliographic record
Abstract
Mechanised harvesting operations are growing in popularity in South Africa, as motor-manual and manual harvesting operations pose significant health and safety risks to workers. Potential damage inflicted by single-grip harvester feed rollers and delimbing knives on the log surface during debranching and debarking of eucalypts may affect chip size distributions during chip production. Chip size is important as it influences pulp quality and recovery in the kraft pulping process. The study investigated the influence of two mechanised debarking treatments in eucalypts (three feed roller passes and five feed roller passes along the stem surface) with feed-roller-induced log surface damage on pulp value recovery. The two mechanised treatments were compared against chips produced from manually debarked logs with no surface damage. In addition, the effect of two log drying periods (one week and two weeks) and three log classes (base, middle and top logs) on chip quality were also analysed. An economic evaluation was conducted to quantify potential recoverable pulp value losses associated with debarking treatments and log drying periods. Logs subject to manual debarking produced significantly less undesired sized chips than both three-pass and five-pass mechanically debarked logs and therefore had significantly greater pulp value recovery. Mechanically debarked logs had a projected pulp value recovery of R62, R86 and R123 less per bone dry tonne of chips produced from base, middle and top logs, respectively, when compared with manually debarked logs with no log surface damage after a one-week log drying period. Mechanically debarked logs also had a projected pulp value recovery of R77, R40 and R59 less per bone dry tonne of chips produced from base, middle and top logs, respectively, when compared with manually debarked logs with no log surface damage after a two-week log drying period. Logs dried for two weeks also produced significantly less under-sized chips than chips produced from one-week-dried logs and therefore had greater pulp value recovery. However, two-week-dried logs produced wood chips with significantly more over-thick chips than logs dried for one week. The volume of undesirable-sized chips produced during chipping increased with decreasing log size.
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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".