Molecular and metalic ions in the magnetosphere: ISSI team preliminary results 
Bibliographic record
Abstract
Molecular and metallic ions are vastly unexplored in near-Earth space because only a few terrestrial missions have been equipped with dedicated instrumentation to separate these molecular and metallic ions, within only a limited energy range (cold ions of < 50 eV and energetic ions of ~100 keV). Nevertheless, existing data from past and on-going missions including those not designed for the required mass separation are capable of detecting many of these ions with available tools, although severe limitations exist (sensitivity and energy range in addition to mass resolution and mass range). By combining these patchy and incomplete data, we found several features that indicate sources of these heavy ions. (1) Combination of Kaguya and Cluster/RAPID during high flux events of solar wind heavy ions suggests that the Moon can be a substantial source for low charge-state metallic ions in the magnetosphere when the Moon is located upstream of the Earth. This interpretation is consistent with Geotail/STICS statistics of increased flux of low charge-state heavy ions near new-Moon for medium activity (Kp=2-4). (2) The major route of molecular ion supply (<10 keV) to the inner magnetosphere can be via low-latitude (< 60° invariant latitude, according to e-POP/IRMS) in addition to the cusp (according to Cluster/CIS and Akebono/SMS) during high outflow flux period. This indicates extraordinary upward convection (or ion flow) at the sub-auroral region. (3) A case study of lidar data during high flux events of solar wind heavy ions suggests that upward expansion of Na signal can be associated with molecular ion escape to the magnetosphere that is also observed by Cluster/RAPID and e-POP/IRM, although this expansion can be related to a major magnetic storm rather than solar wind event.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.004 |
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".