SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals
Bibliographic record
Abstract
This version of SNEWPY was submitted to the Journal of Open Source Software (see pre-review and review). Many thanks to the reviewers and editors! What's Changed since v1.0 Added multiple new models (<code>Zha_2021</code>, <code>Fornax_2019</code>, <code>Fornax_2021</code>, <code>Tamborra_2014</code>, <code>Tamborra_2014</code>, <code>Walk_2018</code>, <code>Walk_2019</code>) Added new Togashi EOS simulation to <code>Nakazato_2013</code> model Easier interface to download available SN model files Redesigned SNOwGLoBES interface (<code>snewpy.snowglobes</code> module) SNEWPY is now available on PyPI via <code>pip install snewpy</code> Added documentation on Read The Docs, improved example notebooks and scripts Various minor bugfixes, performance and other improvements SNEWPY now requires Python 3.7 or newer. New Contributors @joshuashzha made their first contribution in https://github.com/SNEWS2/snewpy/pull/35 @schol made their first contribution in https://github.com/SNEWS2/snewpy/pull/39 @thomahrens made their first contribution in https://github.com/SNEWS2/snewpy/pull/41 @mcolomermolla made their first contribution in https://github.com/SNEWS2/snewpy/pull/56 @jpkneller made their first contribution in https://github.com/SNEWS2/snewpy/pull/93 @sgriswol made their first contribution in https://github.com/SNEWS2/snewpy/pull/105 <strong>Full Changelog</strong>: https://github.com/SNEWS2/snewpy/compare/v1.0.0...v1.1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.494 | 0.022 |
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; both teacher heads agree on what is shown here.
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".