Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ESA’s Swarm mission is a constellation probing both Earth’s interior and geospace, delivering magnetic and plasma measurements which are used to generate many derived data products. From empirical magnetic field models of the core, crust, ionosphere, and magnetosphere, to multi-point estimates of ionospheric currents and in-situ plasma properties, these are challenging to navigate, process, and visualize. The VirES for Swarm platform ( https://vires.services ) has been built to tackle this problem, providing tools to increase usability of Swarm data products. The VirES (Virtual environments for Earth Scientists) platform provides both a graphical web interface and an API to access and visualise Swarm data and models. This is extended with a cloud-hosted development environment powered by JupyterHub (the “Virtual Research Environment/VRE”). VirES provides two API’s: the full VirES API for which a dedicated Python client is provided, viresclient , and the more interoperable Heliophysics API (HAPI). The VRE is furnished with a bespoke Python environment containing thematic libraries supporting science with Swarm. This service aims to ease the pathway for scientists writing computer code to analyze Swarm data products, increase opportunities for collaboration, and leverage cloud technologies. Beyond simply providing data and model access to Python users, it is extremely helpful to provide higher-level analysis and visualization tools, and ready-to-use code recipes that people can explore and extend. Critically for space physics, this involves crossover with many other datasets and so it is highly valuable to embed such tools within the wider data and software ecosystems. Through Swarm DISC (Data, Innovation, and Science Cluster), we are tackling this through cookbooks and Python libraries. Cookbooks are built and presented using Jupyter technologies, and tested to work within the VRE. A new library we are building is SwarmPAL , which includes tools for time-frequency analysis and inversion of magnetic field measurements for electric current systems, among others, while relying on the VirES server to provide data portability and other utilities. This paper reviews the current state of these tools and services for Swarm, particularly in the context of the Python in Heliophysics Community, and the wider heliophysics and geospace data environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it