Comment on “Pulsating Auroras Produced by Interactions of Electrons and Time Domain Structures” by Mozer Et Al.
Bibliographic record
Abstract
Abstract Mozer et al. (2017, https://doi.org/10.1002/2017JA024223 ) suggested that time domain structures (TDSs) drive pulsating aurora (with additional contributions by kinetic Alfvén waves (KAWs)) and that chorus waves have negligible effects. In this comment, we point out that electrons scattered by TDS or KAW (dominantly at ~0.1–3 keV, <1 s modulation) cannot explain key features of pulsating aurora, which require precipitation above a few keV with a couple of tens of second modulation. Their study did not conduct quantitative evaluations of wave‐aurora correlation. The use of short burst mode data (~<10 s) may only cover a single pulse of pulsating aurora and is not suitable for examining connections to pulsating aurora. “Field‐aligned” electrons do not necessarily represent loss cone population, and their characteristic energy (hundreds of eV) is much lower than typical precipitation over pulsating aurora. By reexamining the events studied by Mozer et al., we quantitatively demonstrate that TDS and KAW are uncorrelated with pulsating aurora and that only chorus waves showed high correlations with pulsating aurora. Occasional simultaneous occurrence of TDS/KAW and pulsating aurora is found to be coincidental, because the correlation over a time scale of minutes is poor. Several auroral features analyzed in that paper are not pulsating aurora but other types of aurora. We also show that the chorus‐pulsating aurora correlation can last for 2 h or longer and can be used to highlight dynamic changes in magnetic field mapping. Chorus waves can resonate with electrons above a few keV and are in agreement with pulsating auroral properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".