Finite element modeling of guyed back spars in cable logging
Bibliographic record
Abstract
In this study a finite element model of a back spar system was developed with three guylines opposing the skyline strap tension. In this paper the allowable skyline strap tension is the tension in the skyline strap that results in the maximum normal stress on a transverse cross section of the tree being equal to an assumed allowable stress. An iterative routine was developed to find the allowable skyline strap tension, and this routine was found to converge rapidly from initial values that were below and above the allowable skyline strap tension. Two algorithms were developed for finding the maximum normal stress on a transverse cross section of a tree, method 1 and method 2. If the plane that the tree displaced in was known a priori, then method 2 could be used, and it was found to be less sensitive to mesh coarseness. If the plane that the tree displaced in was not known a priori, then method 1 had to be used with a less coarse mesh. It was found that the stress concentrations due to simplified cable connections were not significant for rigging configurations that allowed a larger rigging point displacement. The rigging configurations that allowed larger rigging point displacements have stress fields that are dominated by bending, while for rigging configurations that allow only small rigging point displacements, the stress fields are dominated by axial compression.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".