Analysing the Impact of Harvesting Methods on the Quantity of Harvesting Residues: An Australian Case Study
Bibliographic record
Abstract
Many parameters can influence the weight of harvesting residues per hectare that remain on plantation sites after extracting sawlogs and pulpwoods. This study aimed at quantifying the impact of the cut-to-length and whole-tree harvesting methods on the weight of harvesting residues using 26 case studies in Australian plantations. A database was created using case studies conducted in different plantations, to measure the weight of harvesting residues per hectare and the components of harvesting residues. An analysis of variance was applied to test the impact made by the harvesting methods. The results confirmed that the cut-to-length harvesting method produced a larger weight of residues (104.0 tonnes of wet matter per hectare (tWM/ha) without additional biomass recovery and 64.7 tWM/ha with additional biomass recovery after sawlog/pulpwood extraction) than the whole-tree harvesting method (12.5 tWM/ha). The fraction test showed that stem wood formed the largest proportion of the harvesting residues in cut-to-length sites and needles were the largest component of the pine harvesting residues in sites cleared by the whole-tree harvesting method. The outcomes of this study could assist plantation managers to set proper strategies for harvesting residues management. Future research could study the impact of product type, silvicultural regime, stand quality, age, equipment, etc., on the weight of harvesting residues.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".