Polar Cap Boundary identification using redline optical data and DMSP satellite particle data
Bibliographic record
Abstract
\justify The location of the polar cap boundary is typically determined using low-orbit satellite measurements in which the boundary is identified by its unique signature of a sharp decrease in energy and particle flux poleward of the auroral oval. In principle, this decrease in precipitating particles should appear as a concomitant sharp change in auroral luminosity. Based on a few events, \cite{Blanchard_1995} suggested that a dramatic gradient in redline aurora may also be an indicator of the polar cap boundary. In recent years, advances in capabilities and the deployment of ground-based all-sky imagers have ushered in a new era of auroral measurements. Auroral imaging has moved well beyond the capabilities of the instrumentation in the previous study in terms of both spatial and temporal resolution. We now have access to decades of optical data from arrays spanning a huge spatial range, enabling a fresh examination of the relationship between redline aurora, particle precipitation, and the polar cap open closed boundary. In this study, we use data from the DMSP satellites in conjunction with the University of Calgary’s REGO (630.0nm) data to assess the viability of automated detection of the 2-dimensional polar cap boundary. Our results exhibit good agreement between the optical and particle polar cap boundary and suggest that a luminosity in redline emission could serve as a reasonable proxy for the location of the the electron poleward boundary during, while providing both high temporal and spatial resolution maps of the open-closed boundary.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".