Productivity and cost analysis of tower yarder systems using the Koller 507 and the Valentini 400 in southwest Germany
Bibliographic record
Abstract
Cable-based timber extraction offers some advantages with regard to impacts to forest stands and soils, and can be used under a wide range of conditions. It is important not only in steep terrain, but also increasingly in flat terrain when soils have low bearing capacity. In this study, utilization data from two commonly used tower yarding systems were analyzed: a tower yarder with a mounted processor (K507) and a medium-distance tower yarder (V400). Collected data included explanatory variables, such as the proportion of hardwood timber, length of skyline, direction of yarding and dimension of harvested timber. Data were analyzed with regard to the time required for machine installation including set-up and dismantling, machine productivity and resulting production costs. Possible combinations of machines and partial working steps were evaluated. Results indicated an increasing utilization of cable crane systems in horizontal yarding direction throughout the analyzed time period. Further, more time was required to process full trees when the K507 was used, although machine productivity increased. The proportion of processed timber that was hardwood significantly influenced installation times. Results demonstrated that, if the machines had above average productivity, total costs could be reduced in flat terrain by using a cable crane instead of conducting the extraction by skidders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".