Steady magnetospheric convection selection criteria: Implications of global SuperDARN convection measurements
Bibliographic record
Abstract
Quantitative definitions have been developed to detect intervals of steady magnetospheric convection (SMC), but these methods are based only on proxies of convection, such as the Auroral Electrojet (AE) indices. For the first time, observations of ionospheric convection on a global scale are studied during intervals identified as steady magnetospheric convection events. The SMC occurrence and SuperDARN transpolar voltage exhibit a strong seasonal bias, which is believed to be due to the seasonal variation of ionospheric conductivity. Equivalent AE values in winter and summer correspond to similar electrojet current strengths but to different levels of convection. Events selected using a constant AE threshold correspond to enhanced convection but above a variable threshold. This SuperDARN study is the first step in improving the reliability of SMC event selection by using convection observations. We present a new variable AE cutoff function to reduce the seasonal dependence of SMC selection on ionospheric conductivity.
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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 | 0.006 | 0.020 |
| 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 | 0.000 | 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 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".