Cable Tensile Forces Associated to Winch Design in Tethered Harvesting Operations: A Case Study from the Pacific North West
Bibliographic record
Abstract
Cable tensile forces in winch-assist harvesting have been investigated in order to assess the safety concerns of the technology. However, the literature is lacking, particularly in regards to the impact of winch design. In this study, a Summit Winch Assist tethering a feller-director on ground slopes up to 77% was monitored for four days. The cable tensile forces were simultaneously recorded at the harvesting and anchor machine at a frequency of 100 Hz. Cameras and GNSS devices enabled a time study of the operations and the recording of machine positions. Winch functionality and design were disclosed by the manufacturer and used for the interpretation of the results. The cable tensile forces reached 296 kN at the harvesting machine and 260 kN at the anchor machine. The slow negotiation of obstacles while moving downhill recorded the highest peaks, mainly due to threshold settings of the winch in the brake system activation. Lower but significant peaks were also recorded during stationary work tasks. The peaks, however, were limited to a few events and never exceeded the endurance limit of the cable. Overall, the study confirmed recent findings in cable tensile force analysis of active winch-assist operations and provided evidence of the underlaying mechanisms that contribute to cable tensile forces.
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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 | 0.001 | 0.001 |
| 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".