Solar wind forcing at Mercury: WSA‐ENLIL model results
Bibliographic record
Abstract
Analysis and interpretation of observations from the MESSENGER spacecraft in orbit about Mercury require knowledge of solar wind “forcing” parameters. We have utilized the Wang‐Sheeley‐Arge (WSA)‐ENLIL solar wind modeling tool in order to calculate the values of interplanetary magnetic field (IMF) strength ( B ), solar wind velocity ( V ) and density ( n ), ram pressure (~ nV 2 ), cross‐magnetosphere electric field ( V × B ), Alfvén Mach number ( M A ), and other derived quantities of relevance for solar wind‐magnetosphere interactions. We have compared upstream MESSENGER IMF and solar wind measurements to see how well the ENLIL model results compare. Such parameters as solar wind dynamic pressure are key for determining the Mercury magnetopause standoff distance, for example. We also use the relatively high‐time‐resolution B ‐field data from MESSENGER to estimate the strength of the product of the solar wind speed and southward IMF strength ( B s ) at Mercury. This product VB s is the electric field that drives many magnetospheric dynamical processes and can be compared with the occurrence of energetic particle bursts within the Mercury magnetosphere. This quantity also serves as input to the global magnetohydrodynamic and kinetic magnetosphere models that are being used to explore magnetospheric and exospheric processes at Mercury. Moreover, this modeling can help assess near‐real‐time magnetospheric behavior for MESSENGER or other mission analysis and/or ground‐based observational campaigns. We demonstrate that this solar wind forcing tool is a crucial step toward bringing heliospheric science expertise to bear on planetary exploration programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".