Skyline tension and dynamic loading for cable yarding comparing conventional single-hitch versus horizontal double-hitch suspension carriages
Bibliographic record
Abstract
Wire rope used in cable logging, where a series of cables facilitate the extraction of timber on steep terrain, experiences high tensions that must be managed to ensure safety. Innovations change practices over time and double-hitch carriages, which allow trees to be extracted horizontally, are a recent example. This makes it feasible to harvest across terrain with limited deflection, increases the recovery of biomass, and potentially reduces high-dynamic load events associated with ground contact. In this study, a standard single-hitch carriage was compared against a new double-hitch carriage under controlled conditions. Tension was measured continuously and specific elements, such as tension when the carriage was at midspan, maximum tension at breakout and inhaul, and magnitude of cyclic tension during inhaul were identified, measured and compared against payload. While payload was similar in the treatments, the additional weight of the double-hitch carriage resulted in higher skyline tensions. In both treatments, payload and skyline tension when the carriage was at midspan were strongly correlated. Cyclic tension was reduced by the double-hitch carriage system. While a number of unique high-dynamic loads were identified that were not part of the normal extraction, they represented only 6% of the cycles and the maximum tension was similar to that experienced during break-out and inhaul. This study increased the understanding of skyline tension during logging operations and the effect of carriage type. It showed that, though tension often exceeds the safe working load of the cable, it does not exceed the endurance limit for a well-designed and operated system.
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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 | 0.001 |
| 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".