Operative loading in cable yarding systems: field observations of static and dynamic tensions in mobile anchor systems
Bibliographic record
Abstract
Cable yarding systems are an effective means of facilitating harvesting operations in steep terrain, but require sufficient anchoring for safe performance. Design and safe operation often dictates that cable loading remain at a safe level, yet the actual loads incurred for anchoring systems, particularly for “mobile” or “equipment” anchors, are ill-quantified. Furthermore, these anchors are subject to loading that is rather dynamic in nature, realizing impulse loads from various operation occurrences, particularly breakout. This study describes the measured static and dynamic cable loads during yarding for 21 mobile anchor systems operated by eight different contractors during active logging operations. The mean dynamic loading observed for guyline and skyline anchors was 49% and 44% greater than static tensions, respectively. Maximum dynamic loads exceeded approximately double static tensions. In the observed tests, cable tensions were well below the cable elastic and endurance limits but did approach the allowable tension in some cases. Although the monitored cable loads in this study were all within a safe range, the observed dynamic loads are useful guidance when considering higher static loads and consequently higher dynamic loads that may be unsafe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".