Tensile Force Monitoring on Large Winch-Assist Forwarders Operating in British Columbia
Bibliographic record
Abstract
The forest industry around the world is facing common challenges in accessing wood fiber on steep terrain. Fully mechanized harvesting systems based on specialized machines, such as winch-assist forwarders, have been specifically developed for improving the harvesting performances in steep grounds. While the mechanization process is recognized as a safety benefit, the use of cables for supporting the machine traction needs a proper investigation. Only a few studies have analyzed the cable tensile forces of winch-assist forwarders during real operations, and none of them focused on large machines normally used in North America. Consequently, a preliminary study focused on tensile force analysis of large winch-assist forwarders was conducted in three sites in the interior of British Columbia during the fall of 2017. The results report that in 86% of the cycles, the maximum working load of the cable was less than one-third of the minimum breaking load. The tensile force analysis showed an expected pattern of minimum tensile forces while the forwarders were traveling or unloading on the road site and high tensile forces when operating on steep trails, loading or traveling. Further analysis found that the maximum cycle tensile forces occurred most frequently when the machines were moving uphill, independently of whether they were empty or loaded. While the forwarders were operating on the trails, slope, travel direction, and distance of the machines from the anchor resulted statistically significant and able to account for 49% of tensile force variability. However, in the same conditions, the operator settings accounted for 77% of the tensile force variability, suggesting the human factor as the main variable in cable tensile force behavior during winch-assist operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".