Adhesion of Wet Snow to Different Cable Surfaces
Bibliographic record
Abstract
Cohesion of snow and its adhesion to cable surfaces are the decisive factors for wet-snow shedding from power-line cables. Knowing the adhesive strength of snow is essential to predict when snow will shed and what consequences it will have on the elements of the transmission line. It also appears to be a basic input for simulating wet-snow shedding. Snow adhesion depends on several parameters, among which snow liquid water content and density, and cable surface geometry were examined experimentally. In particular, the adhesion of wet-snow samples to flat surfaces of different roughness, and to stranded cable surfaces was examined in this study. Two series of experiments were conducted to measure shear adhesive strength as well as tensile adhesive strength of snow. Shear adhesive strength was measured with a centrifuge adhesion test device where a snow sample was placed on a beam, which was then rotated with increasing angular frequency until detachment, the angular frequency at detachment being proportional with shear adhesive force and strength. The tensile adhesive tests were carried out with a material test machine on a semi-spherical snow sample. The sample was compressed slowly at a constant speed until it reached a predefined compressive force limit, and then a tensile load was applied until the detachment of snow. The main observations showed that adhesion was strongest for a critical value of liquid water content, that shear adhesive strength was greatest on stranded cable surfaces, and that tensile adhesive strength was weaker on stranded than on flat cable surfaces.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".