Effects of Establishment and Thinning of Shelterwoods on Harvester Performance
Bibliographic record
Abstract
An increased use of shelterwoods in regeneration has generated a demand for knowledge of how single-grip harvester performance is affected by shelterwood treatments. Time consumption and productivity of a large single-grip harvester working in shelterwood establishment and thinning was studied using work sampling. Five treatments were studied, 1) shelterwood establishment, thinning of 2) sparse, 3) medium and 4) dense shelterwoods and 5) clear-cutting. Each treatment was replicated three times. Results shows that time consumption for the average harvested tree increased with tree volume and declining number of harvested trees per ha. Productivity was higher in clear-cutting than in any of the shelterwood treatments. Harvesting costs in the shelterwood system thus becomes higher than in the clear-cutting system. These costs must be carefully weighted against the ecological and silvicultural benefits of the shelterwood, including the possible reductions of the regeneration costs.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".