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Record W4391427971 · doi:10.1190/tle43020084.1

Opportunities for open-source software to accelerate research in applied geophysics

2024· article· en· W4391427971 on OpenAlexaff
Lindsey J. Heagy, Seogi Kang, Joseph Capriotti, Dominique Fournier, Rowan Cockett, Douglas W. Oldenburg

Bibliographic record

VenueThe Leading Edge · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMira Geoscience (Canada)University of British ColumbiaCanmore Museum and Geoscience CentreUniversity of British Columbia Hospital
Fundersnot available
KeywordsOpen sourceOpen source softwareSoftwareComputer scienceData scienceSoftware engineeringProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.290
GPT teacher head0.386
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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