Bibliographic record
Abstract
Context: The complex interplay between the Solar Wind and the lunar surface serves as a quintessential example of space weathering. However, uncertainties persist regarding the influence of plasma originating from Earth’s ionosphere, necessitating a comprehensive understanding of its quantitative impact. Hitherto, the dearth of reliable models has impeded accurate computation of ion flux from Earth to the Moon under varying solar wind conditions. Aim: The objective of this study is to adapt a kinetic model for the challenging conditions of having both the Earth and the Moon in a single simulation box. Methods: IAPIC, the Particle-In-Cell Electromagnetic Relativistic Global Model was modified to handle the Sun-Earth-Moon system. It employs kinetic simulation techniques that have proven invaluable tools for exploring the intricate dynamics of physical systems across various scales while minimizing the loss of crucial physics information such as backscattering. Results: The modeling allowed to derive the shape and size of the Earth’s magnetosphere and allowed tracking the O + and H + ions escaping from the ionosphere to the Moon: O + tends to escape towards the dayside magnetopause, while H + travels deeper into the magnetotail, extending up to the Lunar surface. In addition, plasma temperature anisotropy and backstreaming ions were simulated, allowing for future comparison with the experiment. Conclusion: This study shows how a kinetic model can successfully be applied to study the transport of ions in the Earth-Moon environment. A second paper will detail the effect on the Lunar environment and the impact on the Lunar water.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".