Ground-based and additional science support for SMILE
Bibliographic record
Abstract
The joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions, and simultaneously monitor the auroral response of the Northern Hemisphere ionosphere. Combining these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SMILE mission. Here, we describe current community efforts to prepare for SMILE, and the benefits and context various experiments that have explicitly expressed support for SMILE can offer. A dedicated group of international scientists representing many different experiment types and geographical locations, the Ground-based and Additional Science Working Group, is facilitating these efforts. Preparations include constructing an online SMILE Data Fusion Facility, the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar, and the consideration of particular observing strategies and spacecraft conjunctions. We anticipate growing interest and community engagement with the SMILE mission, and we welcome novel ideas and insights from the solar-terrestrial community.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.024 |
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".