Dependence of SuperDARN cross polar cap potential upon the solar wind electric field and magnetopause subsolar distance
Bibliographic record
Abstract
Dependence of the cross polar cap potential (CPCP) upon the interplanetary electric field (IEF), solar wind ram pressure, and magnetopause standoff distance (RMS) are investigated by considering the Super Dual Auroral Radar Network (SuperDARN) HF radar data on the CPCP and information on the solar wind and interplanetary magnetic field from ACE satellite. Data of up to IEF ∼12 mV/m are available. The CPCP scatterplot versus IEF shows a linear increase between 0 and ∼3 mV/m. For larger IEFs, the rate of the CPCP growth decreases, and the dependence eventually saturates. If the same data are arranged in groups of RMS values, linear dependences were found for each group, but the slope of the regression line increases with RMS. The inferred linear fit lines of CPCP versus IEF for various RMS groups intersect each other at IEF ∼3 mV/m. This number is found to be unique because for the IEF above it (below it), the CPCP decreases (increases) with the solar wind ram pressure. This value also corresponds to the condition of the solar wind magnetic field at the subsolar point to be equal to the Earth's dipole magnetic field, as previously reported by Siscoe et al. (2002). Presented analysis suggests that the magnetopause standoff distance (the length of the merging X line) is an important factor in the CPCP saturation.
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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 | 0.001 | 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) | 0.001 | 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".