Ouranosinc/raven: v0.19.0
Bibliographic record
Abstract
What's Changed Major Changes The raven library is now available for installation on PyPI (under the name birdhouse-raven) (#536, #569) Documentation has been overhauled so that WPS processes are listed in a more organized manner (#535) Reimplemented the cruft configuration and ported recent changes to ensure that the project is up-to-date (#536) Removed pysheds and watershed delineation process due to a licensing issue (#495) Updated several dependencies to ensure that processes emit fewer warnings and notebooks render properly (#535) Internal Changes Allowed for larger int fields when writing to GeoJSON (#535) Ensured that JSON-serialized output does not include numpy complex types (#535) Added a GitHub Workflow to test the Dockerfile recipe configuration for RavenWPS (#481) Cleaned up the Dockerfile recipe configuration for raven. Now using gunicorn for service management (#481) Testing data fetching mechanism has been refactored to use pooch for better maintainability (#569) Tooling has been updated to use ruff and other newer tools for code quality and formatting (#569) raven now uses Trusted Publisher for TestPyPI and PyPI releases (#569) New Contributors @dependabot[bot] made their first contribution in https://github.com/Ouranosinc/raven/pull/500 Full Changelog: https://github.com/Ouranosinc/raven/compare/v0.18.2...v0.19.0
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.384 | 0.540 |
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".