Reliability of Matching AMPERE Field‐Aligned Current Boundaries With SuperDARN Lower Latitude Ionospheric Convection Boundaries During Geomagnetic Storms
Bibliographic record
Abstract
Abstract High‐latitude ionospheric convection is a useful diagnostic of solar wind‐magnetosphere interactions and nightside activity in the magnetotail. For decades, the high‐latitude convection pattern has been mapped using the Super Dual Auroral Radar Network (SuperDARN), a distribution of ground‐based radars which are capable of measuring line‐of‐sight (l‐o‐s) ionospheric flows. From the l‐o‐s measurements an estimate of the global convection can be obtained. As the SuperDARN coverage is not truly global, it is necessary to constrain the maps when the map fitting is performed. The lower latitude boundary of the convection, known as the Heppner‐Maynard boundary (HMB), provides one such constraint. In the standard SuperDARN fitting, the HMB location is determined directly from the data, but data gaps can make this challenging. In this study we evaluate if the HMB placement can be improved using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), in particular for active time periods when the HMB moves to latitudes below . We find that the boundary as defined by SuperDARN and AMPERE are not always co‐located. SuperDARN performs better when the AMPERE currents are very weak (e.g., during non‐active times) and AMPERE can provide a boundary when there is no SuperDARN scatter. Using three geomagnetic storm events, we show that there is agreement between the SuperDARN and AMPERE boundaries but the SuperDARN‐derived convection boundary mostly lies equatorward of the AMPERE‐derived boundary. We find that disagreements primarily arise due to geometrical factors and a time lag in expansions and contractions of the patterns.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".