Improvements in Value Recovery through Low Stump Heights: Mechanized versus Manual Felling
Bibliographic record
Abstract
Abstract Stump heights were measured on two blocks harvested during the summer of 2000 in north-central British Columbia. Each block was of similar stand and terrain characteristics, consisting mainly of subalpine fir (Abies lasiocarpa) with minor components of white spruce (Picea glauca × P. engelmannii) on gentle slopes. The blocks were harvested by two different contractors using different felling methods: mechanized felling with a feller-buncher and manual felling with a chainsaw. The average measured stump height from mechanized felling was 8.8 cm lower than that of manual felling, measuring 21.9 and 13.1 cm, respectively. When the saw kerf of felling equipment and stump-pull were included, the average stump height of mechanized felling was shown to be 5.8 cm (17%) lower than manual felling. High-end and low-end potential value losses were determined based on average sawlog values (Canadian [CN] $60/m 3 ) and pulp log values (CN$40/m 3 ), respectively. The potential value loss from manual felling was estimated to be up to CN$0.33/tree more than from mechanized felling. This result indicated that mechanized felling recovered up to CN$160/ha over manual felling when an average sawlog value and the stand density information from the study site were used in the calculation. The study demonstrated that lower stump heights than the 30 cm maximum stump height set by the Forest Practices Code of British Columbia are attainable with both felling methods. Sensitivity analysis was performed to determine the potential value and volume gains for a range of stump heights from 0 to 30 cm. Operational constraints were identified in the study, and recommendations for minimizing stump heights are presented.
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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) | 0.002 | 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".