New research capabilities of a fully portable LMA designed for short-fuse targets
Bibliographic record
Abstract
Abstract A new, purely portable Lightning Mapping Array (LMA) has been assembled for deployments ranging in duration from days to months as needed to meet a variety of project goals and enable collaboration with larger, semi-nomadic field projects. The stations have been designed with only a few major components to assemble and check on for deployment, such that the largest restriction for the network deployment time is the time necessary for driving between locations. The array has enabled the sampling of lightning in a variety of storm modes and ground-truth measurements for testing changes to a permanent network. So far, it has been used for (1) a short-term array deployed at various pre-designated locations in the southeast United States with the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project, (2) a short-term array at locations determined by the forecasted landfall location of Hurricane Ian, (3) supplemental components of the existing Oklahoma LMA, and (4) a contribution for a larger LMA throughout a winter season during a winter season field campaign (Lake Effect Electrification (LEE) project). Station design and deployment procedures have been refined throughout these projects to enable smoother array deployments in the future. Network logistics and example applications are shown here.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".