V2DL3 - VERITAS (VEGAS and Eventdisplay) to DL3 Converter.
Bibliographic record
Abstract
V2DL3 - release 0.8.1 of the VERITAS (VEGAS and Eventdisplay) to DL3 Converter. V2DL3 is a tool to convert VERITAS data products to DL3 FITS format, allowing to use e.g. the gammapy science tools for analysis. This is an intermediate release of V2DL3 including the following updates / changes: addition service tools for simplified Eventdisplay analysis. What's Changed Add query anasum runparameters as tool by @GernotMaier in https://github.com/VERITAS-Observatory/V2DL3/pull/241 Remove obsolete docker file for Eventdisplay V2DL3 by @GernotMaier in https://github.com/VERITAS-Observatory/V2DL3/pull/242 Simplify Eventdisplay CI by @GernotMaier in https://github.com/VERITAS-Observatory/V2DL3/pull/243 Remove prepare eventdisplay by @GernotMaier in https://github.com/VERITAS-Observatory/V2DL3/pull/244 Update README with new pypi instructions. by @GernotMaier in https://github.com/VERITAS-Observatory/V2DL3/pull/240 Full Changelog: https://github.com/VERITAS-Observatory/V2DL3/compare/v0.8.0...v0.8.1
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.431 | 0.328 |
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".