Bibliographic record
Abstract
This is a release candidate for Bilby 2.7.0. There are a few major changes/additions in this release along with minor changes and removals. Major changes Likelihood instances can now be called as likelihood.log_likelihood(parameters), see here for more information. Support the new API in dynesty=3 Add a new WaveformGenerator capable of using arbitrary waveform models implemented through the gwsignal waveform interface. Additions Added WeightedCategorical-Prior by @JasperMartins in https://github.com/bilby-dev/bilby/pull/893 ENH: Implement DiscreteValues prior by @unkaktus in https://github.com/bilby-dev/bilby/pull/947 ENH: Allow no parameters as state by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/941 ENH: add support for new dynesty api by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/950 FEAT: add gwsignal waveform generator by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/877 Fixes BUG: Fix sampling efficiency warning by @fgittins in https://github.com/bilby-dev/bilby/pull/953 TYPO: fix random call in example by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/973 BUG: Fix matched filter SNR calculation in time domain injection by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/957 TST: mark whitened strain tests as flaky by @mj-will in https://github.com/bilby-dev/bilby/pull/987 MAINT: update file extension logic by @mj-will in https://github.com/bilby-dev/bilby/pull/962 Changes ENH: plot_multiple: Allow plotting onto user-defined figure by @unkaktus in https://github.com/bilby-dev/bilby/pull/946 MAINT: change RNG imports by @mj-will in https://github.com/bilby-dev/bilby/pull/943 MAINT: np.trapz -> np.trapezoid by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/974 MAINT: define Planck15-LAL cosmology using LAL constants by @mj-will in https://github.com/bilby-dev/bilby/pull/932 DEV: make sure all priors return float when needed by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/979 Replace pytables with h5py by @duncanmmacleod in https://github.com/bilby-dev/bilby/pull/982 Deprecations DEP: deprecate dnest4 interface by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/980 Removed MAINT: remove unsupported roq json weight file format by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/945 Other changes DOC: Fixed README link for opening bilby_pipe issues by @mick-wright in https://github.com/bilby-dev/bilby/pull/922 DOC: Correct docs for use_ratio argument of run_sampler by @mattpitkin in https://github.com/bilby-dev/bilby/pull/958 CI: add merge_group to enable merge queue by @mj-will in https://github.com/bilby-dev/bilby/pull/983 TST: remove dnest4 import test by @ColmTalbot in https://github.com/bilby-dev/bilby/pull/984 BLD: migrate to pyproject.toml by @mj-will in https://github.com/bilby-dev/bilby/pull/952 BLD: use release branches by @mj-will in https://github.com/bilby-dev/bilby/pull/954 New Contributors @fgittins made their first contribution in https://github.com/bilby-dev/bilby/pull/953 @mattpitkin made their first contribution in https://github.com/bilby-dev/bilby/pull/958 @duncanmmacleod made their first contribution in https://github.com/bilby-dev/bilby/pull/982 Full Changelog: https://github.com/bilby-dev/bilby/compare/v2.6.0...v2.7.0rc0
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.462 | 0.590 |
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".