Characteristics of Electron Precipitation During 40 Energetic Electron Injections Inferred via Subionospheric VLF Signal Propagation
Bibliographic record
Abstract
Abstract Energetic electron injection events are associated with energetic electron precipitation (EEP) through possible resonant wave‐particle interactions. Previous studies confirm the impacts of injection‐driven precipitation on observed amplitude/phase of subionospheric VLF (very low frequency) signals transmitted from distant artificial transmitters. Currently, there are substantial uncertainties on precipitation characteristics and flux during injection events. In this work we study 40 injection events selected by Van Allen Probes particle data to investigate the changes in amplitude and phase of VLF signals at ground receivers across Canada during particle injection events. We model the ionospheric effect of the EEP flux to find its impact on VLF propagation and characterize the injection events. Typically, we find a clear phase advance of ~40° in the received VLF signal at Fort Smith (Canada, L = 8) transmitted from U.S. Navy communication transmitter NAA at Maine (USA). Comparing to other VLF transmitter‐receiver paths in North America leads us to conclude that effects are only seen on paths with adequately large range ≫200 km) through L > 7. Modeling the VLF phase change indicates that in the majority of events (>90%), less than 10% of the strong scattering limit inferred from particle flux measurements at the Van Allen Probes is required to obtain the observed VLF phase signature. The median precipitating flux during energetic particle injections is less than 4 × 10 6 el/cm 2 s sr (<10% of the strong scattering rate) for electrons above ~40 keV extracted from trapped particles energy spectrum. This implies that strong scattering is not typical for these 40 selected energetic electron injection events.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".