Sounding of the plasmasphere by Mid‐continent MAgnetoseismic Chain (McMAC) magnetometers
Bibliographic record
Abstract
We present a statistical analysis on the plasmaspheric mass density derived from the field line resonance (FLR) observations by the Mid‐continent MAgnetoseismic Chain (McMAC). McMAC consists of nine stations in the United States and Mexico along the 330° magnetic longitude, spanning L‐values between 1.5 and 3.4. Using the gradient method and an automated procedure for FLR detection, we studied a full year of McMAC observations between July 2006 and June 2007. We find that the rate of FLR detection can reach as high as 56% around local noon at L = 2.7, and the detection rates at higher and lower L‐values decline due to the occasional presence of the plasmapause and weaker FLR signals, respectively. At L‐values between 1.8 and 3.1, the inferred equatorial plasma mass density follows the L‐dependence of L−4. By comparing the mass density with the electron density, we found that the ion mass gradually decreased from 1.7 amu at L = 1.8 to 1 amu at L = 3.1. The plasma mass density exhibits an annual variation that maximizes in January, and at L = 2.4 the ratio between January and July densities is 1.6. Our observations also show a local time dependence of plasmaspheric mass density that stays steady in the morning and rises postnoon, a phenomenon that may be attributed to the equatorial ionization anomaly as a part of the plasma neutral coupling at low latitude.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".