pytroll/satpy: Version v0.20.0 (2020/02/25)
Bibliographic record
Abstract
Issues Closed Issue 1077 - Tropomi l2 reader needs to handle more filenames (PR 1078) Issue 1076 - Metop level 2 EUMETCAST BUFR reader (PR 1079) Issue 1004 - Computing the lons and lats of metop granules from the eps_l1b reader is painfully slow (PR 1063) Issue 1002 - Resampling of long passes of metop l1b eps data gives strange results Issue 928 - Satpy Writer 'geotiff' exists but could not be loaded Issue 924 - eps_l1b reader does not accept more than 1 veadr element (PR 1063) Issue 809 - Update avhrr_l1b_aapp reader (PR 811) Issue 112 - Python 2 Cruft (PR 1047) In this release 8 issues were closed. Pull Requests Merged Bugs fixed PR 1084 - Add latitude_bounds and longitude_bounds to tropomi_l2 PR 1078 - Tropomi l2 reader to handle more types of products (1077) PR 1072 - Fix the omerc-bb area to use a sphere as ellps PR 1066 - Rename natural_color_sun to natural_color in generic VIS/IR RGB recipes PR 1063 - Fix eps infinite loop (924, 1004) PR 1058 - Work around changes in xarray 0.15 PR 1057 - lowercase the sensor name PR 1055 - Fix sst standard name PR 1049 - Fix handling of paths with forward slashes on Windows PR 1048 - Fix AMI L1b reader incorrectly grouping files PR 1045 - Update hrpt.py for new pygac syntax PR 1043 - Update seviri icare reader that handles differing dataset versions PR 1042 - Replace a unicode hyphen in the glm_l2 reader PR 1041 - Unify Dataset attribute naming in SEVIRI L2 BUFR-reader Features added PR 1082 - Update SLSTR composites PR 1079 - Metop level 2 EUMETCAST BUFR reader (1076) PR 1067 - Add GOES-17 support to the 'geocat' reader PR 1065 - Add AHI airmass, ash, dust, fog, and night_microphysics RGBs PR 1064 - Adjust default blending in DayNightCompositor PR 1061 - Add support for NUCAPS Science EDRs PR 1052 - Delegate dask delays to pyninjotiff PR 1047 - Remove deprecated abstractproperty usage (112) PR 1020 - Feature Sentinel-3 Level-2 SST PR 988 - Remove py27 tests and switch to py38 PR 964 - Update SEVIRI L2 BUFR reader to handle BUFR products from EUMETSAT Data Centre PR 839 - Add support of colorbar PR 811 - Daskify and test avhrr_l1b_aapp reader (809) Documentation changes PR 1068 - Fix a typo in writer 'filename' documentation PR 1056 - Fix name of natural_color composite in quickstart Backwards incompatible changes PR 1066 - Rename natural_color_sun to natural_color in generic VIS/IR RGB recipes PR 988 - Remove py27 tests and switch to py38 In this release 31 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.401 | 0.507 |
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".