CORHI-X: a Python tool to investigate heliospheric events through multiple observation angles and heliocentric distances
Bibliographic record
Abstract
The CORonagraph and Heliospheric Imager data eXplorer (CORHI-X) is an open-source tool designed for the Heliophysics community to foster multi-spacecraft studies. CORHI-X enables users to easily visualize multi-spacecraft constellations, their overlapping fields of view (FoV), and the occurrence of coronal mass ejections (CMEs) over defined time intervals beginning in 2019. CORHI-X is accessible via a Python Streamlit interface, where users can select coronagraphs (e.g., STEREO-A/COR1-COR2, SOHO/C2-C3, SOLAR ORBITER/Metis) and heliospheric imagers (e.g., STEREO-A/HI-A, PARKER SOLAR PROBE/WISPR, SOLAR ORBITER/SoloHI). The tool not only provides spacecraft positions and FoVs of selected instruments, but also ensures that FoVs are plotted only when the corresponding spacecraft data are available. Indeed, the online archives of each instrument are checked monthly to retrieve observation dates, ensuring that CORHI-X automatically incorporates the latest data. For an effective search for eruptive events potentially appearing within the instrument’s FoVs, CORHI-X is linked to two different CME catalogs (DONKI and HI-Geo, respectively). The user can visualize specific CMEs and propagate them over distance to identify which events may have entered the FoV of one or more instruments. Users can also manually enter their own CME input parameters (propagation direction, speed, time, width, and longitude) via the interface. For propagation, a simple drag-based model is incorporated in the visualization of the spacecraft constellation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.027 |
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".