Bibliographic record
Abstract
What's Changed DFT shift fix for new scikit, speed improvement for find_nearest by @IainHammond in https://github.com/vortex-exoplanet/VIP/pull/638 Minor bug fixes: In SNR map calculation in the case of integer input fwhm by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/639 For PCA-RDI with 4D cubes by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/642 For mixed array types when creating bad pixel maps A few new features by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/642 Added compatibility for NEGFC combined with either PCA-ARDI or PCA RDI with data imputation IPCA-ARDI now does PCA-ARDI with the initial number of principal components, instead of RDI with the last number of principal components Made frame_by_frame mode of cube_fix_badpix_isolated function compatible with input bad pixel map Wider multiprocessing support (now compatible with 'spawn' starting method when 'fork' not available) by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/643 Bug fix for IPCA-ARDI with DI initialization by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/643 Documentation: Updated readthedocs.yml by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/633, https://github.com/vortex-exoplanet/VIP/pull/634 & https://github.com/vortex-exoplanet/VIP/pull/635 Updated dosctrings by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/640 Added sphinx rtd theme to requirements by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/636 Added info about VIP conventions by @VChristiaens in https://github.com/vortex-exoplanet/VIP/pull/637 Full Changelog: https://github.com/vortex-exoplanet/VIP/compare/v1.6.0...v1.6.1
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.245 | 0.283 |
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".