Key factors influencing productivity of whole-tree ground-based felling equipment commonly used in the Pacific Northwest
Bibliographic record
Abstract
Around the globe, various types of forest machinery are employed to conduct fully mechanized ground-based timber harvesting. In the Pacific Northwest, the whole-tree harvesting method remains dominant. While machine-integrated sensors provide accurate productivity information in the cut-to-length harvesting method, productivity is more complicated to determine in whole-tree harvesting. This literature review compiles and analyses the existing evidence on productivity studies of feller–bunchers and feller–directors in a systematic manner and identifies the factors influencing machine productivity. The study indicates that most of the previous research was conducted in North America, particularly in Canada. It was also found that a considerable portion of the literature lacked statistical analysis. Piece size, slope, and silvicultural treatment were the most commonly studied productivity-influencing factors among the results. Although there is already a general understanding of the most important factors influencing the productivity of feller–bunchers and feller–directors, there is still a lack of accurate measurement and isolation of individual factors to facilitate accurate productivity prediction. Further research is needed for the development of systems that use integrated sensors capable of estimating machine productivity. Updated productivity models will optimize harvesting operations, identify bottlenecks, and allow for the development of best practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".