Bibliographic record
Abstract
How the solar wind and the Earth's magnetosphere interact, involving kinetic, fluid and global scales processes, is one of the key questions in space plasma physics. In situ instruments on a fleet of solar wind and magnetospheric constellation missions now provide the most detailed observations of Sun-Earth connections over multiple scales, from the smallest of a few kilometres up to the largest of a few 10s of Earth radii. However, we are still unable to fully quantify the global effects of the drivers of such connections, including the conditions that prevail throughout geospace due to the limitations of point measurements. This information is the key missing link for developing a complete understanding of how the Sun gives rise to and controls Earth's plasma environment and space weather. This is where SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) comes in.SMILE is a novel self-standing mission dedicated to observing the solar wind - magnetosphere coupling via simultaneous in situ solar ion and magnetic field measurements, soft X-ray imaging of the magnetosheath, magnetopause and polar cusps, and UV imaging of the northern hemisphere auroral oval. Remote sensing of the magnetosheath and cusps with soft X-ray imaging is made possible thanks to solar wind charge exchange (SWCX) X-ray emissions known to occur in the vicinity of the Earth's magnetosphere. SMILE is a joint mission between ESA and the Chinese Academy of Sciences (CAS) due for launch in the middle of 2025. SMILE science objectives as well as the latest scientific and technical developments, jointly undertaken by ESA, CAS and the international instrument teams, will be presented. SMILE will be complemented by ground-based observatories, in particular the newly funded Canada Space Agency SMILE Imaging Science project, as well as by theory and simulation investigations. This presentation will be dedicated to Graziella Branduardi-Raymont, SMILE mission Co-proposer and mission Co-PI, who passed away on 3rd November 2023.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".