The Poynting vector applied to the complex refractive index in a hot plasma near the electron cyclotron frequency and to the cyclotron resonance observed on topside ionograms
Bibliographic record
Abstract
The electron cyclotron resonance is the only fundamental resonance observed on topside ionograms that, up to the present, has not been understood. There is a solution to the hot plasma dispersion equations for the wave frequency f near the cyclotron frequency fH when the wave number k makes a small angle to the Earth's magnetic induction B. For this case, the magnitude of the imaginary part of k is more than half the real part. This means that the group velocity, dω/dk, where ω = 2πf, is complex. The real part of dω/dk is too large to explain the cyclotron resonance observations. In addition to the wave number, the dispersion relations for a hot magnetoplasma can be used to obtain the electric and magnetic fields of the cyclotron wave and hence the Poynting vector. Dividing the Poynting energy flux by the energy density of the wavefield, where the energy density is the sum of the electric, magnetic, and electron energy densities, gives the Poynting velocity VP, which is a good approximation to the actual wave energy velocity for f near fH. Cyclotron waves are strongly damped but can exist several milliseconds as observed. The waves radiate from the plane containing B and the dipole antenna L. The waves with f > fH beat with those with f < fH to produce the beat frequency observed on fixed‐frequency ionograms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".