Ionospheric feedback instability and substorm development
Bibliographic record
Abstract
We report on ground magnetic and optical observations performed during an ionospheric heating experiment at the High Frequency Active Auroral Research Program (HAARP) facility in Alaska on 29 October 2008. The experiment was aimed at generation of large‐amplitude ULF electromagnetic waves by triggering and facilitating development of the ionospheric feedback instability (IFI) in the region adjacent to a bright auroral arc. In this region the downward/return magnetic field‐aligned current decreases plasma density and enhances the electric field in the ionosphere. A combination of these two effects creates favorable conditions for the instability. The experiment occurred during a period of substorm activity, but effects from the HAARP transmitter were not sufficiently intense to be detected against the background of strong natural oscillations occurring farther north from the HAARP site. Thus the experiment did not provide concrete evidence that heating of the ionosphere with powerful HF transmitters can affect IFI development or generate intense ULF electromagnetic waves. However, during the experiment ground‐based magnetometers in Alaska and Canada detected large‐amplitude ULF waves in regions where the substorm onset auroral arcs interacted with the ionosphere. The frequencies of these waves closely matched frequencies predicted by simulations of IFI for these particular geophysical conditions. These observations support the hypothesis that geomagnetic substorms, the corresponding dynamics of discrete auroral arcs, and the ionospheric feedback instability are closely connected phenomena.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".