Bibliographic record
Abstract
This release of xESMF improves support for parallelization with dask: weights can now be computed in parallel, and those weights can be applied over chunks spanning the horizontal grid dimensions. Previously, computing weights in parallel was only possible using MPI, and datasets could only be chunked over non-spatial dimensions. These new features are the outcome of Charles Gauthier' internship at Ouranos during the summer of 2023. Thanks to Charles for his hard work and sharp analysis, which led to a permanent position at Ouranos! What's Changed Remove uppercase in longitude/latitude for test by @raphaeldussin in https://github.com/pangeo-data/xESMF/pull/259 Fix broken link by @rcaneill in https://github.com/pangeo-data/xESMF/pull/255 Perform ci tests with python 3.11 now that numba is compatible. by @charlesgauthier-udm in https://github.com/pangeo-data/xESMF/pull/272 Bump mamba-org/provision-with-micromamba from 15 to 16 by @dependabot in https://github.com/pangeo-data/xESMF/pull/266 Removed 3.7 from supported versions. Build docs using 3.9 by @huard in https://github.com/pangeo-data/xESMF/pull/271 Added w property to Regridder and SpatialAverager by @huard in https://github.com/pangeo-data/xESMF/pull/276 Adding the ability to use dask arrays with chunks along spatial axes by @charlesgauthier-udm in https://github.com/pangeo-data/xESMF/pull/280 Repare broken links to earthsystemcog by @huard in https://github.com/pangeo-data/xESMF/pull/292 Parallel weight generation with Dask by @charlesgauthier-udm in https://github.com/pangeo-data/xESMF/pull/290 Replace if statements by dict.get to reduce number of code switches by @huard in https://github.com/pangeo-data/xESMF/pull/295 Warn of SpatialAverager error over large region and densify polygons by @charlesgauthier-udm in https://github.com/pangeo-data/xESMF/pull/293 New Contributors @rcaneill made their first contribution in https://github.com/pangeo-data/xESMF/pull/255 @charlesgauthier-udm made their first contribution in https://github.com/pangeo-data/xESMF/pull/272 Full Changelog: https://github.com/pangeo-data/xESMF/compare/v0.7.1...v0.8
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.377 |
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".