Plasma-neutral gas interactions in various space environments: Assessment beyond simplified approximations as a Voyage 2050 theme
Bibliographic record
Abstract
Abstract In the White Paper, submitted in response to the European Space Agency (ESA) Voyage 2050 Call, we present the importance of advancing our knowledge of plasma-neutral gas interactions, and of deepening our understanding of the partially ionized environments that are ubiquitous in the upper atmospheres of planets and moons, and elsewhere in space. In future space missions, the above task requires addressing the following fundamental questions: (A) How and by how much do plasma-neutral gas interactions influence the re-distribution of externally provided energy to the composing species? (B) How and by how much do plasma-neutral gas interactions contribute toward the growth of heavy complex molecules and biomolecules? Answering these questions is an absolute prerequisite for addressing the long-standing questions of atmospheric escape, the origin of biomolecules, and their role in the evolution of planets, moons, or comets, under the influence of energy sources in the form of electromagnetic and corpuscular radiation, because low-energy ion-neutral cross-sections in space cannot be reproduced quantitatively in laboratories for conditions of satisfying, particularly, (1) low-temperatures, (2) tenuous or strong gradients or layered media, and (3) in low-gravity plasma. Measurements with a minimum core instrument package (< 15 kg) can be used to perform such investigations in many different conditions and should be included in all deep-space missions. These investigations, if specific ranges of background parameters are considered, can also be pursued for Earth, Mars, and Venus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".