Lightning hazard estimation by integrating surface electromagnetic and physical properties
Bibliographic record
Abstract
We propose a method to estimate lightning hazard by integrating various physical surface properties and an electromagnetic parameter in order to present a lighting hazard map of northern Alberta, Canada. Physical surface properties include the land class, roughness, and temperature; whereas the electromagnetic parameter implies the estimated dielectric constant in this study. Geographic information system (GIS) data mining and spectral correlation methods are mainly carried out to estimate the potential lightning strike and consequent lightning hazard over the study area. The GIS data mining technique is implemented to find out the rule between the physical surface properties at each pixel and the lighting records. We compute the relative frequencies of the rules containing three different physical surface properties and sort them to identify which rule retains the highest possibility of lightning strikes. The potential lightning strike map is generated by normalizing the derived frequencies ranging from 0 to 1 and used with the non-hierarchical dielectric constant map in order to extract the pixels satisfying the condition of high dielectric constant and high frequency of a lightning strike by the wavenumner correlation filtering (WCF) method. The two maps filtered by the WCF are then combined by the local favorability index (LFI) to enhance the result. By correlating the potential lightning strike map with the non-hierarchical dielectric constant values in the spectral domain using the WCF and integrating them by the LFI, a lightning hazard of the study area is presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".