seisbench/seisbench: SeisBench v0.10 - SkyNet, SeisDAE and a more powerful model API
Bibliographic record
Abstract
Major updates The SkyNet model is now available in SeisBench. SkyNet is specifically designed to pick phase arrivals at regional distances (up to 20 degree) and also comes with a set of weights to distinguish between Pn, Pg, Sn and Sg phases. With SeisDAE, there's now a second denoising model available in SeisBench. In addition, there are plenty of new functions to train denoising model and a new tutorial notebook walking you through the process step by step. A few tweaks make the use of models more powerful and convenient. The to_preferred_device function automatically moves the model to CUDA or Apple Silicon if available. Overlaps can now be specified as fractions of the input length instead of sample. In addition, models can now have dynamic input length depending on the data. Thanks to everyone how contributed to this release with feedback, issues, and especially with PRs! What's Changed skynet integration by @albertleonardo in https://github.com/seisbench/seisbench/pull/355 Enable specifying overlaps in annotate as fractions instead (Closes #353) by @yetinam in https://github.com/seisbench/seisbench/pull/357 Updated model training tutorial by @yetinam in https://github.com/seisbench/seisbench/pull/361 Enable models with dynamic input and output length by @yetinam in https://github.com/seisbench/seisbench/pull/363 Training an own Denoiser model by @JanisHe in https://github.com/seisbench/seisbench/pull/360 Add to_preferred_device function by @yetinam in https://github.com/seisbench/seisbench/pull/362 pre-commit: using ruff by @miili in https://github.com/seisbench/seisbench/pull/368 Full Changelog: https://github.com/seisbench/seisbench/compare/v0.9.0...v0.10.0
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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) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.181 | 0.225 |
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".