Effects of substorm dynamics on magnetic signatures of the ionospheric Alfvén resonator
Bibliographic record
Abstract
Spectral resonance structures (SRS) of the ionospheric Alfvén resonator (IAR) measured by the induction magnetometer at the High Frequency Active Auroral Research Program (HAARP) ionospheric observatory during a substorm on 28 February 2006 are presented. The evolution of IAR SRS is compared to ionospheric parameters measured by the colocated Digisonde, riometer and all‐sky imager at HAARP. Initially, the magnetic IAR signatures (spectral resonance structures) exhibited an expected variation that can be attributed to typical diurnal changes in ionospheric structure. At substorm onset, the signatures disappeared because of either a suppression of resonance conditions by substorm‐related particle precipitation or enhanced power in the Pc1 spectrum that concealed continuing IAR SRS. After the substorm, the SRS reappeared; however the harmonics had shifted to lower frequencies with tighter frequency spacing. For the first time, we show that this time‐dependent behavior in IAR SRS is explained by increased F region densities resulting from electron precipitation. Similarities between observed IAR harmonic frequencies and those calculated with a model suggest that variations in F region density, especially foF2, may often dominate the evolution of IAR eigenfrequencies. This could potentially provide a mechanism for monitoring topside dynamics using IAR SRS.
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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) | 0.000 | 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 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".