SubMachine ORFEUS integration: Web-based tools for exploring seismic tomography models
Bibliographic record
Abstract
SubMachine is a collection of web-based tools for the interactive visualisation, analysis, and quantitative comparison of global-scale datasets of the Earth's interior [1]. It focuses on making regional and global seismic tomography models easily accessible to the wider solid Earth community to facilitate collaborative exploration. Over 30 tomography models can be visualised and explored—individually, side-by-side, or through statistical and averaging tools. SubMachine also serves diverse non-tomographic datasets, including plate reconstruction models, normal mode observations, global crustal structure, shear wave splitting, geoid, marine gravity, vertical gravity gradients, and global topography in adjustable degrees of spherical harmonic resolution.To ensure continuity beyond the DEEP TIME ERC project [2], SubMachine is transitioning to a new home within ORFEUS (Observatories and Research Facilities for European Seismology, http://orfeus-eu.org/). This transition secures SubMachine’s long-term sustainability and further integrates it into the broader seismological research infrastructure.In preparation for this move, SubMachine has undergone significant modernization. The entire platform has been migrated to Python 3.12. The transition from Basemap to Cartopy enhances long-term stability, though some projections may experience slower performance. New features include cross-sections through vote maps [3]. These advancements, along with various performance improvements, position SubMachine as a more robust and sustainable resource for the geoscience community.[1] Hosseini, K., Matthews, K. J., Sigloch, K., Shephard, G. E., Domeier, M., & Tsekhmistrenko, M. (2018). SubMachine: Web-Based Tools for Exploring Seismic Tomography and Other Models of Earth's Deep Interior. Geochemistry, Geophysics, Geosystems, 19(5), 1464-1483.[2] https://cordis.europa.eu/project/id/833275[3] Shephard, G. E., Matthews, K. J., Hosseini, K., & Domeier, M. (2017). On the consistency of seismically imaged lower mantle slabs. Scientific reports, 7(1), 10976.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.026 |
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".