Accuracy of Transmission Line Modeling Based on Aerial LiDAR Survey
Bibliographic record
Abstract
Aerial LiDAR survey is receiving wide application in transmission-line modeling due to its efficiency. The technique is particularly useful for modeling of existing lines for the purpose of thermal rating, upgrading, or vegetation management. An accurate modeling of an existing line depends largely on proper determination of the base conductor temperature, i.e. the conductor temperature at the time of the aerial light detection and ranging (LiDAR) survey. In this paper, an acceptable accuracy for the base conductor temperature is first established. Extensive parametric studies are then conducted to reveal the effects of all the potentially major factors: ambient air temperature, electrical load, solar radiation, wind, and conductor size on the base conductor temperature. As a result, recommendations are made on the proper practice of performing an aerial LiDAR survey and determining the base conductor temperature so that the resulting transmission line modeling is within an acceptable accuracy. It is demonstrated that a wide error can easily be introduced without following a proper procedure for the LiDAR survey.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".