Predicting the Type of Narrow Bipolar Pulse: A Machine Learning Approach
Bibliographic record
Abstract
Abstract Narrow Bipolar Pulses (NBP) depicts the electric field (E-Field) changes due to a Compact Intra-cloud Discharge (CID) and are of two types namely Positive NBP (PNBP) and Negative NBP (NNBP). In this study, 437 NBPs were statistically investigated using a dataset collected in Sri Lanka in 2013, 2015, 2016 and 2017. Seven independent variables (Pulse Duration (PD), Rise Time (RT), Slow Front Duration (SFD), Zero Crossing Time (ZCT), Full Width at Half Maximum (FWHM) and Ratio between the Initial and Overshoot Peak Amplitudes (RIOPA)) and one dependent variable (Type of NBP) were analyzed. Two machine learning classification models, the Random Forest (RF) model and the Binary Logistic Regression (BLR) model, were used to predict the dependent variable based on the independent variables. Two models were compared in terms accuracy (ACC), sensitivity (SE), specificity (SP), AUC (Area Under the Curve)-ROC (Receiver Operator Characteristic) curve and kappa statistic. RF model scored the highest in terms of ACC (0.91), specificity (0.94), Kappa statistics (0.82) and AUC (0.98). In conclusion, RF model had the best performance in predicting the type of NBP hence can be used as a suitable automated method to classify the type of NBP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".