Shared Software Environments for Heliophysics.
Bibliographic record
Abstract
Making software work across platforms is currently a challenging engineering task but modern approaches to software engineering and development are making it attainable. In the past decades shared software environments have come into a mature state. It is possible to create and use these shared software environments on different platforms. This greatly facilitates sharing and using software between computers. The premise of these shared software environments is that they ‘just work’, regardless of the platform, and decrease user time spent resolving dependencies and related software installation challenges. Furthermore, we believe that open science, which has the goal of creating transparent and reproducible results, will require such solutions since much of our research today relies on creating and/or repurposing existing software to do science. In this paper we describe the efforts of the IHDEA Science Platforms group to engineer a small set of basic shared environments for doing heliophysics research using Python software. These software environments include a range of implementations ranging from virtual environments to containerized environments as well as container-backed platforms, like JupyterLab, which may be hosted remotely in the cloud or institutional compute environments. We present use cases, our work to identify key software in use by the community, our analysis to understand the types of shared software environments that would be of the most use to the community
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".