Statistical analysis of EMIC waves in plasmaspheric plumes from Cluster observations
Bibliographic record
Abstract
Abstract Recently, electromagnetic ion cyclotron (EMIC) wave generation in plasmaspheric plumes has been the subject of extensive discussion. Theory predicts that regions of detached cold, dense plasma immersed in relatively low background magnetic field should aid EMIC wave growth and may provide conditions for interaction between the EMIC waves and relativistic (MeV) electrons, leading to energetic particle loss into the atmosphere. Since plasmaspheric plumes are specific to disturbed geomagnetic conditions, the link between EMIC waves and plumes may be especially important for radiation belt dynamics during magnetic storms. In this work, we present an in situ survey of EMIC waves in plasmaspheric plumes using data from the Cluster satellites and will address the question of whether plumes are important for EMIC wave generation from a statistical perspective. We used a survey of plasmaspheric plumes between 2001 and 2006 identified from the Waves of High frequency and Sounder for Probing of Electron density by Relaxation (WHISPER) sounder measurements. We further identified EMIC waves from simultaneous (with WHISPER) magnetic field measurements by the fluxgate magnetometer instruments and investigated the relationship between these two data sets. Only 10% of the time when Cluster‐observed plumes along its orbit did we also observe EMIC waves. The wave occurrence outside plumes is further significantly reduced and is ~20 times lower in immediately adjacent regions than inside plumes. We found that cold plasma density was not a good predictor of EMIC occurrence inside the plumes and that the absolute density does not affect the EMIC probability. On the other hand, enhanced solar wind dynamic pressure significantly increases EMIC wave occurrence rate inside the plumes.
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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 | 0.001 | 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) | 0.000 | 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".