On the Persistent Shape and Coherence of Pulsating Auroral Patches
Bibliographic record
Abstract
Abstract The pulsating aurora covers a broad range of fluctuating shapes that are poorly characterized. The purpose of this paper is therefore to provide objective and quantitative measures of the extent to which pulsating auroral patches maintain their shape, drift and fluctuate in a coherent fashion. We present results from a careful analysis of pulsating auroral patches using all‐sky cameras. We have identified four well‐defined individual patches that we follow in the patch frame of reference. In this way we avoid the space‐time ambiguity which complicates rocket and satellite measurements. We find that the shape of the patches is remarkably persistent with 85–100% of the patch being repeated for 4.5–8.5 min. Each of the three largest patches has a temporal correlation with a negative dependence on distance, and thus does not fluctuate in a coherent fashion. A time‐delayed response within the patches indicates that the so‐called streaming mode might explain the incoherency. The patches appear to drift differently from the SuperDARN‐determined convection velocity. However, in a nonrotating reference frame the patches drift with 230–287 m/s in a north eastward direction, which is what typically could be expected for the convection return flow.
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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".