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Record W6948784682 · doi:10.5281/zenodo.10520045

Ouranosinc/xscen: v0.8.0

2024· other· en· W6948784682 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsOuranos
Fundersnot available
KeywordsNetCDFPascal (unit)Function (biology)Variable (mathematics)Point (geometry)Filter (signal processing)Resampling

Abstract

fetched live from OpenAlex

Changelog v0.8.0 (2024-01-16) Contributors to this version: Gabriel Rondeau-Genesse (@RondeauG), Pascal Bourgault (@aulemahal), Juliette Lavoie (@juliettelavoie), Sarah-Claude Bourdeau-Goulet (@sarahclaude), Trevor James Smith (@Zeitsperre), Marco Braun (@vindelico). Announcements xscen now adheres to PEPs 517/518/621 using the setuptools and setuptools-scm backend for building and packaging. (PR/292). New features and enhancements New function xscen.indicators.select_inds_for_avail_vars to filter the indicators that can be calculated with the variables available in a xarray.Dataset. (PR/291). Replaced aggregation function climatological_mean() with climatological_op() offering more types of operations to aggregate over climatological periods. (PR/290) Added the ability to search for simulations that reach a given warming level. (PR/251). xs.spatial_mean now accepts the region="global" keyword to perform a global average (GH/94, PR/260). xs.spatial_mean with method='xESMF' will also automatically segmentize polygons (down to a 1° resolution) to ensure a correct average (PR/260). Added documentation for require_all_on in search_data_catalogs. (PR/263). xs.save_to_table and xs.io.to_table to transform datasets and arrays to DataFrames, but with support for multi-columns, multi-sheets and localized table of content generation. Better xs.extract.resample : support for weighted resampling operations when starting with frequencies coarser than daily and missing timesteps/values handling. (GH/80, GH/93, PR/265). New argument attribute_weights to generate_weights to allow for custom weights. (PR/252). xs.io.round_bits to round floating point variable up to a number of bits, allowing for a better compression. This can be combined with the saving step through argument "bitround" of save_to_netcdf and save_to_zarr. (PR/266). Added annual global tas timeseries for CMIP6's models CMCC-ESM2 (ssp245, ssp370, ssp585), EC-Earth3-CC (ssp245, ssp585), KACE-1-0-G (ssp245, ssp370, ssp585) and TaiESM1 (ssp245, ssp370). Moved global tas database to a netCDF file. (GH/268, PR/270). Implemented support for multiple levels and models in xs.subset_warming_level. Better support for DataArray and DataFrame in xs.get_warming_level. (PR/270). Added the ability to directly provide an ensemble dataset to xs.ensemble_stats. (PR/299). Added support in xs.ensemble_stats for the new robustness-related functions available in xclim. (PR/299). New function xs.ensembles.get_partition_input (PR/289). Breaking changes climatological_mean() has been replaced with climatological_op() and will be abandoned in a future version. (PR/290) experiment_weights argument in generate_weights was renamed to balance_experiments. (PR/252). New argument attribute_weights to generate_weights to allow for custom weights. (PR/252). For a sequence of models, the output of xs.get_warming_level is now a list. Revert to a dictionary with output='selected' (PR/270). The global average temperature database is now a netCDF, custom databases must follow the same format (PR/270). Bug fixes Fixed a bug in xs.search_data_catalogs when searching for fixed fields and specific experiments/members. (PR/251). Fixed a bug in the documentation build configuration that prevented stable/latest and tagged documentation builds from resolving on ReadTheDocs. (PR/256). Fixed get_warming_level to avoid incomplete matches. (PR/269). search_data_catalogs now eliminates anything that matches any entry in exclusions. (GH/275, PR/280). Fixed a bug in xs.scripting.save_and_update where build_path_kwargs was ignored when trying to guess the file format. (PR/282). Add a warning to xs.extract._dispatch_historical_to_future. (GH/286, PR/287). Modify use_cftime for the calendar conversion in to_dataset. (GH/303, PR/289). Internal changes Continued work on adding tests. (PR/251). Fixed pre-commit's pretty-format-json hook so that it ignores notebooks. (PR/254). Fixed the labeler so docs/CI isn't automatically added for contributions by new collaborators. (PR/254). Made it so that tests are no longer treated as an installable package. (PR/248). Renamed the pytest marker from requires_docs to requires_netcdf. (PR/248). Included the documentation in the source distribution, while excluding the NetCDF files. (PR/248). Reduced the size of the files in /docs/notebooks/samples and changed the notebooks and tests accordingly. (GH/247, PR/248). Added a new xscen.testing module with the datablock_3d function previously located in /tests/conftest.py. (PR/248). New function xscen.testing.fake_data to generate fake data for testing. (PR/248). xESMF 0.8 Regridder and SpatialAverager argument out_chunks is now accepted by xs.regrid_dataset and xs.spatial_mean. (PR/260). Testing, Packaging, and CI adjustments. (PR/274): xscen builds now install in a tox environment with conda-provided ESMF in GitHub Workflows. tox now offers a method for installing esmpy from a tag/branch (via ESMF_VERSION environment variable). $ make translate is now called on ReadTheDocs and within tox. Linters are now called by order of most common failures first, to speed up the CI. Manifest.in is much more specific about what is installed. Re-adds a dev recipe to the setup.py. Multiple improvements to the docstrings and type annotations. (PR/282). pip check in conda builds in GitHub workflows have been temporarily set to always pass. (PR/288). The cookiecutter template has been updated to the latest commit via cruft. (PR/292): setup.py has been mostly hollowed-out, save for the babel-related translation function. pyproject.toml has been added, with most package configurations migrated into it. HISTORY.rst has been renamed to CHANGES.rst. actions-version-updater.yml has been added to automate the versioning of the package. pre-commit hooks have been updated to the latest versions; check-toml and toml-sort have been added to cleanup the pyproject.toml file, and check-json-schema has been added to ensure GitHub and ReadTheDocs workflow files are valid. ruff has been added to the linting tools to replace most flake8 and pydocstyle verifications. tox builds are more pure Python environment/PyPI-friendly. xscen now uses Trusted Publishing for TestPyPI and PyPI uploads. Linting checks now examine the testing folder, function complexity, and alphabetical order of __all__ lists. (PR/292). publish_release_notes now uses better logic for finding and reformatting the CHANGES.rst file. (PR/292). bump2version version-bumping utility was replaced by bump-my-version. (PR/292). Documentation build checks no longer fail due to broken external links; Notebooks are now nested and numbered. (PR/304).

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.372
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0060.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.3720.538

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.

Opus teacher head0.056
GPT teacher head0.205
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreSoftware

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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