Interplanetary coronal mass ejections from MESSENGER orbital observations at Mercury
Bibliographic record
Abstract
Abstract We use observations from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft, in orbit around Mercury, to investigate interplanetary coronal mass ejections (ICMEs) near 0.3 AU. MESSENGER is the first spacecraft since Helios 1 and 2 in the 1980s to make in situ measurements of the interplanetary medium at heliocentric distances < 0.5 AU. As such, it presents a unique opportunity for observing the innermost heliosphere. It also allows for observations of ICMEs well within 1 AU to study their evolution as they expand and propagate outward, interacting with the solar wind. We catalog ICME events observed by the MESSENGER Magnetometer between 2011 and 2014 and present statistical analyses of ICME properties at Mercury. In addition, using existing data sets of ICMEs at 1 AU, we investigate key ICME property changes from Mercury to 1 AU. We find good agreement with previous studies for the magnetic field strength dependence on distance, and we also find evidence that ICME deceleration continues past the orbit of Mercury. This paper describes the database of ICMEs from MESSENGER orbital observations around Mercury, which is publicly available through the supporting information (Table S1) associated with this manuscript and the Virtual Energetic Particle Observatory. Our ICME database will prove particularly useful for multipoint spacecraft studies of recent ICMEs, as well as for model validation of ICME properties.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".