Bibliographic record
Abstract
Issues Closed Issue 1669 - Cropping a country from an earth image using latitude and longitude coordinate Issue 1667 - Extracting data/ reading data from .DAT file Issue 1664 - Nan values when resample with Kompsat Issue 1656 - Cannot load datasets of multiple SEVIRI native files (PR 1663) Issue 1650 - wrong gamma for red beam of cira_fire_temperature RGB (PR 1662) Issue 1641 - UnicodeDecodeError and ValueError when passing local FSFile to abi_l1b Issue 1635 - The crop function is no longer working. Issue 1633 - Auxiliary offline download doesn't work for modifiers (PR 1634) Issue 1632 - Can't resample GOES Meso data when using night IR composite (PR 1643) Issue 1626 - problem with read UMETSAT Issue 1601 - Allow MiRS reader to apply limb correction optionally (PR 1621) Issue 1594 - slstr_l2: Failed to filter out correct files using find_files_and_readers() with start_time and end_time Issue 1562 - Improve Scene.copy wishlist handling when datasets to copy are specified (PR 1630) Issue 1495 - Values of reflectance In this release 14 issues were closed. Pull Requests Merged Bugs fixed PR 1665 - Fix fci l2 tests on windows PR 1663 - Ignore raw metadata when combining metadata (1656) PR 1662 - Fix cira fire temperature and green snow (1650) PR 1655 - Apply valid_range in MiRS reader when present PR 1644 - Add id for GOMS3/Electro-l n3 PR 1643 - Fix combine_metadata not handling lists of different sizes (1632) PR 1640 - Fix AAPP l1b reader for negative slope on channel 2 (332) PR 1634 - Fix offline aux download not working for modifiers (1633) PR 1631 - Fix satellite altitude being in kilometers in ABI L2 reader PR 1630 - Fix Scene.copy not preserving wishlist properly (1562) PR 1578 - Fix nightly/unstable CI URL Features added PR 1659 - Add SEVIRI + NWC SAF GEO VIS/IR cloud overlay composite PR 1657 - Add parallax-corrected file patterns to the nwcsaf-geo reader PR 1646 - Add new piecewise_linear_stretch enhancement method PR 1636 - Add first benchmarks (uses asv) PR 1623 - Add the reinhard enhancements PR 1621 - Add limb_correction keyword argument to MiRS reader (1601) PR 1620 - Add feature to StaticImageCompositor to allow filenames relative to Satpy 'data_dir' PR 1560 - Allow custom dataset names in 'generic_image' reader and fix nodata handling In this release 19 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.328 | 0.358 |
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".