Bibliographic record
Abstract
What's Changed Near complete re-implementation of the halo catalogue class hierarchy (https://github.com/pynbody/pynbody/pull/763) This is a breaking change, and as such the major version number of pynbody has increased. Regular users probably don't want to install a v2 release at this early stage, and as such it has been flagged as a beta. Halo classes are no longer imported directly into the pynbody.halo namespace. Users may now request a particular halo finder, or list of halo finders, by passing a priority kwarg to SimSnap.halos() HaloCatalogue.precalculate() has been renamed to HaloCatalogue.load_all() Halo classes supporting child/parent relationships now expose a .subhalos property, in which the child halos are enumerated Behind the scenes, the mapping from iords to file offsets has been improved and unified across all HaloCatalogue subclasses GrpCatalogue has been renamed to HaloNumberCatalogue to better reflect its meaning AHFCatalogue no longer renumbers the halos using a one-based scheme by default. If AHF has written out halo numbers, these are used by default; if it has not written out halo numbers, a zero-based indexing is used. Users can pass halo_numbers='v1' to halos() to obtain the old behaviour Full Changelog: https://github.com/pynbody/pynbody/compare/v1.6.0...v2.0.0-beta
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.290 | 0.477 |
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".