High energy particle interactions with the Jovian satellites as observed from Juno
Bibliographic record
Abstract
The MAG investigation’s dedicated star tracker, the Advanced Stellar Compass (ASC), has continuously monitored high energy particles fluxes in Jupiter’s magnetosphere subsequent to Juno’s orbit insertion on July 4, 2016. The instrument performs this function by tracking the effects of radiation with sufficient energy to transit the instrument’s radiation shielding. Such particles have energy >15MeV for electrons, >80MeV for protons, and >~GeV for heavier elements. With a sample cadence of 250ms, the ASC renders a detailed mapping of the trapped particles throughout space traversed by Juno. The Jovian satellites and rings have a profound effect on the measured fluxes. The observed signature from each satellite differs with the physical properties and environment of the moon, such as presence of a magnetic field, volcanism, etc. The line of apsides of Juno’s orbit constantly evolves, as does the magnetic latitude at which Juno transits the satellite orbits, providing a good sampling of longitudinal phases between a given Moon and Juno over time. At just past the midpoint of the nominal mission, all major moons are profiled, including the shepherd moons Metis, Amalthea, Thebe, and, their associated rings. We present examples of these interactions and the implications these observations have for the physical properties of the moons, the density of Jupiter’s dust rings, and the Io torus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".