pytroll/pyresample: Version 1.24.0 (2022/07/06)
Bibliographic record
Abstract
Issues Closed Issue 417 - Add get_abs_max (and get_abs_min) to BucketResampler (PR 418 by @gerritholl) Issue 316 - Upgrade to pyresample 1.17.0 causes IndexError with one-dimensional data (PR 324 by @pnuu) Issue 171 - Update AreaDefinition to accept pyproj CRS objects and WKT In this release 3 issues were closed. Pull Requests Merged Bugs fixed PR 324 - Fix bilinear resampler for 1D data (316) Features added PR 435 - Fix SwathDefinition causing unnecessary dask computes when used as a dict key PR 418 - Implement get_abs_max on BucketResampler (417) PR 368 - Speed up Bucket get_min and get_max PR 341 - Dask resampler and gradient search overhaul Documentation changes PR 429 - Improve docs for dump and load_area_from_string PR 427 - Add Cython classifier to package metadata In this release 7 pull requests were closed.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.360 | 0.484 |
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".