Convergent data sharpening for the identification and tracking of spatial temporal centers of lightning activity
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study presents an exploratory analysis of Ontario lightning and fire ignition data . Our main goal is to relate forest fire ignitions to lightning stroke occurrences. However, due to the sheer volume of the lightning data, as well as accuracy and missing data issues, changes to the data are required prior to any such investigation. Planning to employ cluster‐based point‐process methods in future lightning‐caused fire ignition models, we wish to cluster the lightning strokes in space‐time. The data used is © 1992, 1994, 1997, Queen's Printer for Ontario, Canada, and was referenced under agreement with the Ontario Ministry of Natural Resources. We propose a mode‐seeking clustering algorithm that is based on a convergent form of ‘data sharpening’ methods. Data sharpening is based on local constant regression and was introduced as a bias‐reduction method in kernel density estimation. Data sharpening nudges observations closer to their nearest local mode(s) at each iteration. We propose to iterate the algorithm until convergence, showing that the data will converge to either local or global modes. The usefulness of the algorithm in the lightning context is threefold: first, the lightning data can be reduced to corresponding local spatial‐temporal modes; second, slight modifications result in a noise‐reduction method that can be applied to estimate short‐term spatial track(s) of lightning storm system(s); third, the sharpened data provide a means for a bootstrap‐based simulation of spatial lightning strike patterns. Numerical examples and comments on the algorithm's appropriateness related to the lightning application appear throughout. The study concludes by noting some of the further work to be done. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 |
| 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 it