Bibliographic record
Abstract
What's Changed date: 2024-03 Breaking Changes Summary New package architecture. New toolchain with flit and ruff. clt standard variable uint changed. clone method added to Indicators. Changes Details [maint] BREAKING CHANGE Created a _core package which should not be used outside icclim source. Its content may change without deprecation warnings. [enh] Added DCSC indices under icclim.dcsc namespace. [doc] Adapt Christian's notebooks from ISENES and add them as tutorials in our documentation. [doc] Add nbshpinx extension to render jupyter notebooks in the online documentation. [maint] Migrate from [black, blackdoc, flake8, isort, pyupgrade, pydocstyle] to ruff [maint] Migrate from setup.py to pyproject.toml [maint] Make readthedocs build fail when there are warnings [maint] Fix warnings in doc build [maint] BREAKING CHANGE Update architecture to have a src/ and a tests/ directory at root level [maint] BREAKING CHANGE Migrate build toolchain from setuptools to flit [maint] Remove version number from constants module as it was causing the build process to import icclim. The version number is now statically set in src/icclim/init.py [fix] Force xarray to read dataset sequentially to avoid a netcdf-c threading issue causing seg faults. [enh] Add publish-to-pypi.yml github action to automatically build and publish icclim to pypi. This action is triggered by a github release being published. This action requires a manual approval on github. [enh] Add the following ECAD indices: PP (average of pressure), SS (sum of sunshine) and RH (average of humidity). [fix] BREAKING CHANGE the default unit of clt standard variable is now % as expected (was a wind strenght unit). [maint] BREAKING CHANGE Rework architecture to have a _core private package containing the core logic of icclim. Idea taken from numpy 2.0. [doc] Add docstring to almost all functions, classes and modules. This has been done to improve the generated documentation. co-authored: Github's copilot. [maint] Add missing type hints. [doc] Make use of readthedocs' autoapi library to generate the API documentation. [maint] Improve maintainability of tools/extract-icclim-funs.py to ease adding new registries (still not perfect). [fix] Add clone method to Indicators to avoid modifying the original instance when setting templating metadata. Full Changelog: https://github.com/cerfacs-globc/icclim/compare/v6.5.0...v7.0.0
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.388 | 0.537 |
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".