Temporal characteristics and energy deposition of pulsating auroral patches
Bibliographic record
Abstract
Abstract We present a careful statistical analysis of pulsating aurora (PA) using all‐sky green line (557.7 nm) images obtained at 3.3 Hz. Six well‐defined individual PA patches are identified and extracted using a contouring technique. Quantitative parameters such as the patch duration (on‐time and off‐time), peak intensity, and integrated intensity are determined for each patch and each pulsation. The resulting characteristics serve as strict observational constraints that any of the many competing theories attempting to explain PA must predict. The purpose of this paper is to determine the characteristics of PA patches in order to provide better observational constraints on the suggested mechanisms. All aspects of the temporal behavior of the individual patches appear to be erratic. Historically, PA has been defined very loosely and we argue that the use of the term “pulsating” is inappropriate since our findings and other published results are not regularly periodic and thus a more appropriate term may be fluctuating aurora. Further, we find that the observational constraints do not fit well with the flow cyclotron maser theory, which in particular is suggested to create PA patches. There is no clear candidate of the suggested mechanisms and drivers to explain the observational constraints set by the PA patches in a satisfactory manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".