On O<sup>+</sup> Ion Heating by BBELF Waves at Low Altitude: Test Particle Simulations
Bibliographic record
Abstract
Abstract We investigate mechanisms of wave particle heating of ionospheric O+ ions resulting from broadband extremely low frequency (BBELF) waves using numerical test particle simulations that take into account ion‐neutral collisions, in order to explain observations from the Enhanced Polar Outflow Probe (e‐POP) satellite at low altitudes (∼400 km) (Shen et al., 2018, https://doi.org/10.1002/2017JA024955 ). We argue that in order to reproduce ion temperatures observed at e‐POP altitudes, the most effective ion heating mechanism is through cyclotron acceleration by short‐scale electrostatic ion cyclotron (EIC) waves with perpendicular wavelengths λ⊥ ≤ 200 m. The interplay between finite perpendicular wavelengths, wave amplitudes, and ion‐neutral collision frequencies collectively determine the ionospheric ion heating limit, which begins to decrease sharply with decreasing altitude below approximately 500 km, where the ratio becomes larger than 10−3, νc and fci denoting the O+‐O collision frequency and ion cyclotron frequency. We derive, both numerically and analytically, the ion gyroradius limit from heating by an EIC wave at half the cyclotron frequency. The limit is 0.28λ⊥. The ion gyroradius limit from an EIC wave can be surpassed either through adding waves with different λ⊥ or through stochastic “breakout,” meaning ions diffuse in energy beyond the gyroradius limit due to stochastic heating from large‐amplitude waves. Our two‐dimensional simulations indicate that small‐scale (<1 km) Alfvén waves cannot account for the observed ion heating through trapping or stochastic heating.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".