Cable tensile force monitoring in steep slope forest operations \n
Bibliographic record
Abstract
The interest in active sustainable management of mountain forests is increasing around the world. Great effort, in particular, is focusing on the identification of efficient, safe, and sustainable steep slope harvesting solutions adapted to different environmental conditions. The main attention is moving toward winch-assist harvesting systems on trafficable terrain and European designed standing skyline mobile tower yarders on non-trafficable terrain. These cable-supported harvesting solutions have similar safety issues related to the use of tensioned wire ropes and the use of natural anchors. This research focused on the analysis of cable tensile force while operating with these harvesting solutions in ordinary harvesting sites and operational conditions. \nFirst, a state-of-the-art about the steep slope cable-supported harvesting solutions and their main safety issues was completed. A particular focus was dedicated to the significant works on cable tensile force monitoring. Following, field studies were successfully carried out to monitor the cable tensile forces on ordinary logging operations. The survey interested eleven mobile tower yarders, observed on twelve different cable lines in the Italian Alps, and three large integrated-winch forwarders, observed on eight trails in the interior of British Columbia (Canada). A similar approach, based on the integration of tensile force monitoring, video recording of the operations, ground profile analysis, and load measurement/estimation, was applied to both the cable-supported systems. The case studies, the methodological approach used for the data collection and analysis, and the results of the monitoring are presented and reported in detail. \n
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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 | 0.001 | 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".