Comparison of entropy-based characterization of lightning strike maps using planar and spherical coordinates
Bibliographic record
Abstract
In this paper, effects of including the curvature of the Earth in the characterization of the lightning strike maps (LSMs) are studied. Lightning strikes can adversely affect the power generation and distribution systems, and behaviour prediction of such LSMs is of interest for protection and planning in the power industry. Characterization is one of the important stages of the behaviour prediction. We have shown that multifractal measures such as the Renyi fractal dimension spectrum (RS) are appropriate for characterization of such LSMs which are self-affine. The computation of the RS is based on estimating the probability density function (pdf) of the lightning strikes distribution. Since the LSMs range from very large to low densities over a large geographical area such as Manitoba, computing this pdf in planar coordinates is subject to a nonlinear error due to the curvature of the Earth. Thus, modelling and calculating the maps in spherical coordinates yields a better estimation of the pdf. The data of the LSMs have been collected by the Canadian Lightning Detection Network (CLDN) during the year 2002. The LSMs of Manitoba are characterized through regular (using planar coordinates) and modified (using spherical coordinates) RS techniques. The results indicate that regular RS technique alters the characteristics of the feature space, especially in latitudinal direction, and causes sparser features in the feature space, while using the modified RS technique alleviates all these problems, and provides a more accurate characterization which is necessary for a reliable classification of the maps
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".