A Source or a Sink? How Trends in Particle Precipitation Dictate Electrodynamics in High-Latitude Ionosphere
Bibliographic record
Abstract
Abstract. Fast, charged particles intermittently rain down into Earth's dense atmosphere. The kinetic energy of these particles are converted into heat and light, and it ionizes the atmospheric gas, providing a source of both free and bound energy for the ionosphere; this is the aurora borealis and australis. The specific kinetic energy of the constituent particles in the aurora dictates the atmospheric response to the ongoing particle precipitation, with hard (high-energy) particles penetrating deeper than those that are considered soft (possessing a low kinetic energy). In this paper, we analyze a large database of precipitating particle observations from the United States' Defense Meteorological Satellite Program, and aggregate the altitude-dependent response of the ionosphere at high-latitudes, using fast ionization rate parameterizations due to two important papers by Fang et al. (10.1029/2010GL045406 and 10.1002/jgra.50484). We explore a characteristic altitude-dependent pattern in space (magnetic latitude and longitude), and time (geomagnetic activity), pertaining to the shape of the northern hemisphere, high-latitude ionosphere during local winter. We briefly discuss the implied ratio of E- to F-region Pedersen conductance, and this ratio's ramifications for the growth & decay (and thus proliferation) of plasma turbulence in the high-latitude ionosphere.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".