If substorm onset triggers tail reconnection, what triggers substorm onset?
Bibliographic record
Abstract
Despite the claim that tail reconnection triggers substorm onset, there is an abundance of cases wherein substorm onset triggers tail reconnection. In such cases, the first observable precursor to onset is a periodic rippling (beads) along an equatorward auroral arc. In this study, through an example, we show that substorms arising out of arcs of this type have the classical “inside‐out” evolution, including the triggering of tail reconnection as a possible result. We then investigate what the magnetospheric mode underlying the ripples along the arcs might be. The classical MHD ballooning invoked by some substorm theories is inconsistent with the observation, which exhibits a finite azimuthal wavelength comparable to the local ion gyroradius and the propensity of onset to occur under moderately high (1–10) rather than extremely high plasmaβ. We show that the onset is due to a modified ballooning mode, subject to corrections by the General Ohm's Law and ion heat flux. The net result is that the necessary condition for the instability remains unchanged from the classical MHD, but the growth rate of the instability is heavily attenuated or quenched in the high β and short azimuthal wavelength limits. In the actual magnetosphere, the mode has a wavelength ∼1,500 km, growth timescale ∼10 s, and critical plasma beta in the 3–13 range, all consistent with observations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".