Bibliographic record
Abstract
Release 1.14.0 22 July 2020, included in HEALPix 3.70 Fixed FITS files that were left open https://github.com/healpy/healpy/pull/631 Line Integral Convolution plots to plot polarization https://github.com/healpy/healpy/pull/617 reworked verbose, see hp.disable_warnings https://github.com/healpy/healpy/pull/630 increased precision in coordinate transforms https://github.com/healpy/healpy/pull/633 colormaps now are not overwritten by plotting functions https://github.com/healpy/healpy/pull/627 fix propagation on mmax in smoothing https://github.com/healpy/healpy/pull/612 updated HEALPix C++ to 3.70 https://github.com/healpy/healpy/pull/632 Updated to cfitsio 3.48 (used only if missing) https://github.com/healpy/healpy/pull/597 Local datapath for pixel weights https://github.com/healpy/healpy/pull/611 Support pixel weights for NSIDE 8192 https://github.com/healpy/healpy/pull/595 Minor bugfixes https://github.com/healpy/healpy/pull/626, https://github.com/healpy/healpy/pull/624, https://github.com/healpy/healpy/pull/618, https://github.com/healpy/healpy/pull/614
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.512 | 0.700 |
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".