Theoretical relations between model parameters and Metis observables for eruptive prominences and coronal mass ejections
Bibliographic record
Abstract
Context. We investigate the behavior of the plasma in eruptive prominences and coronal mass ejections in characteristic physical conditions. Aims. We aim to demonstrate various relations between the plasma parameters and radiation properties relevant to Solar Orbiter and Metis observations. Methods. Our method is based on 2D non-local thermodynamic equilibrium (non-LTE) modeling of moving structures that are externally illuminated from the solar disk. We have focused on temperatures below 105 K and a range of gas pressures to investigate the opacity effects in the Lα line. Overall, we applied a large grid of isothermal and isobaric models. Results. Our results are presented in the form of various correlation plots showing relationships between the plasma parameters and radiation properties relevant to Metis observations. We also demonstrate the relative importance of radiative and collisional ionization of hydrogen and the ratio between radiative and collisional excitation of the Lα line. Our results point to the need to carry out optically thick non-LTE modeling for specific plasma conditions. The visible light (VL) emission was also obtained for the purposes of a comparison with the Metis data.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".