Bibliographic record
Abstract
This article discusses that an engineering firm is using software to ensure the structural integrity of all types of pole designs. West Coast Engineering (WCE) Group in Delta, British Columbia, Canada, performed several linear stress analyses using software to optimize the insulator bracket, which supports the transmission lines on the tangent poles. Physical testing was used to verify the accuracy of the analysis results. WCE began the structural analyses by analyzing the shafts of each pole type under ultimate loading, which was determined by Ian Hayward International using standard industry calculations. WCE performed linear static stress analyses on the models and evaluated the von Mises stress criteria for ductile materials to assess the stress results. With the pole shaft and base plate structures verified, engineers focused the next analysis on the insulator brackets of the tangent structure to optimize the load bearing capability and material thickness. With the predictable loading capacity requirements confirmed for the designs, WCE expanded the study to include a simulation of the impact loading that can result from a head-on vehicle collision. WCE is continuing the use of Algor software in the design of poles and in the development of new pole manufacturing equipment. Currently, the company is using it to simulate and optimize a roll forming process.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".