Using ultra-low frequency waves and their characteristics to diagnose key physics of substorm onset
Bibliographic record
Abstract
Substorm onset is marked in the ionosphere by the sudden brightening of an existing auroral arc or the creation of a new auroral arc. Also present is the formation of auroral beads, proposed to play a key role in the detonation of the substorm, as well as the development of the large-scale substorm current wedge (SCW), invoked to carry the current diversion. Both these phenomena, auroral beads and the SCW, have been intimately related to ultra-low frequency (ULF) waves of specific frequencies as observed by ground-based magnetometers. We present a case study of the absolute and relative timing of Pi1 and Pi2 ULF wave bands with regard to a small substorm expansion phase onset. We find that there is both a location and frequency dependence for the onset of ULF waves. A clear epicentre is observed in specific wave frequencies concurrent with the brightening of the substorm onset arc and the presence of "auroral beads". At higher and lower wave frequencies, different epicentre patterns are revealed, which we conclude demonstrate different characteristics of the onset process; at higher frequencies, this epicentre may demonstrate phase mixing, and at intermediate and lower frequencies these epicentres are characteristic of auroral beads and cold plasma approximation of the "Tamao travel time" from near-earth neutral line reconnection and formation of the SCW.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".