Research on pattern recognition and spatial prediction method of lightning activity distribution based on cluster analysis
Bibliographic record
Abstract
Frequent lightning activity has the potential to cause damage to man-made facilities, cause forest fires and other hazards, and the prediction of lightning activity can help to avoid the occurrence of these disasters.In this paper, based on the lightning activity data of a region, the distribution pattern of lightning activity is identified at different elevations and latitudes and longitudes.Then geodetic distance and contributing nearest-neighbor similarity are introduced, and a GS-DBSCAN clustering algorithm is proposed to realize the spatial prediction of lightning activity by using the method of leastsquares fitting of prediction equations.The lightning activity directions after data clustering show topographic correlation, and the overlap between lightning activity directions and topography is about 35%.Combined with the prediction images, it is found that the lightning activity prediction results of this paper's method are closer to the real value than other algorithms, with an average offset error of less than 1.1km, an accuracy rate of >85%, and a false alarm rate of <35%, which reflects a good prediction performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".