Predicting the Climatic Resistance of the Material and the Durability of the Structural Elements of the Composite Tower of the Power Line
Bibliographic record
Abstract
Comparative analysis of two models for predicting residual resource of polymer composite, one proposed by specialists from RS Technologies Inc and the other proposed by Institute of Oil and Gas Problems SB RAS (Russia), was carried out to obtain service life of composite poles for power transmission lines manufactured by RS Technologies Inc (Canada) for cold climate of Republic of Sakha (Yakutia). Models are based on experimental studies of strength of materials during accelerated and full-scale climatic tests. The difference lies in the presence of parameters of climatic zone and test methods in the first model, while the second model considers changes in physical and mechanical properties and structure of materials during aging under conditions of full-scale exposure and accelerated climatic tests. Comparison of results of predicting the durability of fiberglass products in cold climate of Yakutsk (Russia) according to Institute of Oil and Gas Problems SB RAS model and similar products in Calgary (Canada) climate (the closest in terms of climate conditions) according to RS Technologies Inc model showed the same results. Service life of composite support material was approximately 120 years with specified level of permissible decrease in characteristic property index of 75% of the original.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".