pytroll/pyresample: Version 1.26.1 (2023/02/07)
Bibliographic record
Abstract
Issues Closed Issue 497 - resampling fails with ValueError if not padding data Issue 492 - Infinite values in geostationary bounding box crash intersection function (PR 493 by @mraspaud) Issue 486 - BUG: Import fails due to misconfigured setup.py (PR 487 by @bzah) Issue 484 - Fails to build with Shapely 2.0 (PR 485 by @sebastic) Issue 481 - Intermittent failures on 32bit architectures Issue 448 - Release 1.25.1 missing usual GPG signature In this release 6 issues were closed. Pull Requests Merged Bugs fixed PR 493 - Fix geostationary bbox having inf values (2368, 492) PR 487 - FIX: Update pyproj min version to 3.0.0 (486) PR 485 - Tune tolerance to fix test failures with PROJ 9.1.1. (484) PR 482 - Fix intermittent EWA test failures In this release 4 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 0.244 |
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".