Exploring the Use of Harvesters in Large-Diameter Hardwood-Dominated Stands
Bibliographic record
Abstract
The use of fully-mechanized operations, normally targeted at coniferous species, has also been on the rise in mixed-species and continuous-cover forests comprised of a strong share of deciduous species. With special form characteristics (complex crowns, large-diameter branches, forks and sweeps, high wood density, etc.), deciduous trees can lead to wide-ranging harvesting productivities, often divergent from those originally derived from coniferous species. Due to the importance and growing interest in mechanizing operations in close-to-nature mixedwood and deciduous stands, obtaining insight on harvesting productivity in large-diameter deciduous trees was of interest. This study located in Bavaria, Germany, monitored four harvesters (two wheeled and two tracked machines) operated in four distinct harvest blocks (case studies), all of which had a high percentage of large-diameter European beech and oak trees. Harvesting productivity and volume recovery was assessed and quantified. Based on the field inventory of European beech and oak trees and continuous time-and-motion study, average harvesting productivity ranged from 29 to 43 m3/PMH0 (productive machine hours without delay), whereas volume recovery fluctuated between 73% and 85% for trees that were completely felled and processed by machines. Because of the rather limited sample size and the variable conditions between case studies, results should only be used as general orientation on the performance of the tested machines and additional research is suggested to further understand the influence of tree form characteristics on impediments to mechanized processing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".