Open‐source system for geophysical data processing and visualization
Bibliographic record
Abstract
Owing to its affordability and flexibility, open-source software offers substantial benefits yet it is often limited in scope and lacks user interfaces. For a number of years, we have been developing an open-source package (called SIA) for processing and analysis of a broad range of geophysical data, with emphasis on reflection/refraction seismics. In particular, interfaces for virtually any Seismic UNIX and Disco codes have been created. The package represents a highly integrated framework for developing geophysical applications software using C++ and other languages. Currently, the package consists of a processing core extending the capabilities of a seismic processing system, graphical interface, and customizable 3D OpenGL visualization server similar to those used in seismic interpretation. These three components operate in parallel on a distributed computer grid and communicate via the Parallel Virtual Machine. Additionally, through its operating remotely as a web service, SIA offers ways to build web collaboration tools. The system also includes a unique code distribution system which provides a simplified installation and automatic updates to the entire package or its parts.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.141 | 0.123 |
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".