Opportunities for open-source software to accelerate research in applied geophysics
Bibliographic record
Abstract
Abstract The potential for open-source software and open-science practices to accelerate research in applied geophysics and thereby contribute to solutions of geoscientific problems impacting society is considered. We provide context on the definition of open source and give a brief history of open-source software in applied geophysics. Drawing from our experience on the SimPEG project, which develops software for simulation and inversion of geophysical data, we provide two examples where research was accelerated because of open-source software. These include the reuse of regularization methods for different geophysical problems (magnetics and time-domain electromagnetics) and the combination of multiple geophysical data types in joint inversions. We also provide an example where research code was repurposed for education and humanitarian projects. Each of these examples was made possible because of the availability of code and the practices adopted by the community of collaborators involved in the project. We conclude with our perspective on how practices adopted by open-source communities that enable collaboration among researchers with different backgrounds, skills, and interests can be applied more broadly in research. This will ultimately increase the use and effectiveness of geophysics in helping solve applied problems.
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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 | 0.120 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".