High and low ionospheric conductivity standing guided Alfvén wave eigenfrequencies: A model for plasma density mapping
Bibliographic record
Abstract
Numerical solutions to the guided toroidal Alfvén wave equation in a dipole field are presented with symmetric ionospheric Pedersen conductivities ΣPN,S, and using a range of field‐aligned plasma density profiles ∝ 1/rp, where p takes integer values from 0 to 6. We show that weakly damped fundamental guided toroidal Alfvén waves can occur when ΣPN,S are both less than or greater than a critical value. Our results show that for low p values the equatorial plasma density inferred from a measurement of a fundamental mode wave frequency using the high ΣPN,S (“fixed end”) solution can be up to a factor of 4 different from that determined from the low ΣPN,S (“free end”) solution. These results illustrate that in order to correctly invert wave frequency observations and to obtain accurate estimates of the equatorial plasma mass density in the nighttime sector where ΣPN,S may drop below a critical conductivity, the numerical solution of the guided toroidal Alfvén wave equation must be used. In addition, we present results which can be used to scale equatorial plasma densities derived from observed eigenperiods using the analytic WKBJ solutions, with p = 6, into the equatorial plasma densities derived from the solution of the toroidal wave equation for any integer value of p, from 0 to 6, in both the daytime (high ΣPN,S) sectors and nighttime (low ΣPN,S) sectors of the magnetosphere.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".