Solar wind-magnetosphere coupling during HILDCAAs
Bibliographic record
Abstract
High-Intensity Long-Duration Continuous AE Activity (HILDCAA) intervals are driven by High Speed solar wind Streams (HSSs) during which the rapidly-varying interplanetary magnetic field (IMF) produces high but intermittent dayside reconnection rates. This results in several days of large, quasi-periodic enhancements in the auroral electrojet (AE) index. There has been debate over whether the enhancements in AE are produced by substorms or whether HILDCAAs represent a distinct class of magnetospheric dynamics. We investigate sixteen HILDCAA events using the expanding/contracting polar cap model as a framework to understand the magnetospheric dynamics occurring during HSSs. Each HILDCAA onset shows variations in open magnetic flux, dayside and nightside reconnection rates, the cross-polar cap potential, and AL that are characteristic of substorms. The enhancements in AE are produced by activity in the pre-midnight sector, which is the typical substorm onset region. The periodicities present in the intermittent IMF determine the exact nature of the activity, producing a range of behaviours from a sequence of isolated substorms, through substorms which merge into one-another, to almost continuous geomagnetic activity. The magnitude of magnetic fluctuations, $dB/dt$, in the pre-midnight sector during HSSs is sufficient to produce a significant risk of Geomagnetically Induced Currents, which can be detrimental to power-grids and pipelines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".