Ionospheric feedback instability and active discrete auroral forms
Bibliographic record
Abstract
Abstract We present results from time‐dependent, three‐dimensional numerical simulations of ULF Alfvén waves generated by the ionospheric feedback instability at high latitudes. The goal of this study is to understand physical mechanisms responsible for the formation of active discrete auroral forms (curls and folds) typically observed during the substorm onset. Our simulations demonstrate that active feedback of the auroral ionosphere on magnetic field‐aligned currents carried by ULF Alfvén waves can explain wide variety of auroral structures. The main reason for this variety is a susceptibility of the electrodynamics of the coupled magnetosphere‐ionosphere system to parameters of the electric field and plasma in the ionosphere and magnetosphere of the Earth. Due to the highly nonlinear character of the magnetosphere‐ionosphere interactions, different combinations of these parameters lead to different spatial and temporal behavior of the magnetic field‐aligned currents producing aurora. One of the main conclusions from our study is that the ionosphere is responsible for the formation of small‐scale curls and folds in the discrete aurora and for the intensification of magnetic field‐aligned currents, observed during substorms. The results from our 3‐D simulations are applied for the explanation of structure of discrete auroral arcs observed during the 29 October 2013 substorm at Fort Yukon, Alaska.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".