Bibliographic record
Abstract
Pipeline analysis utilities for radio astronomy and cosmology. Bug Fixes config: incorrect exception error message (645b47c) memh5: change _make_selections to be a classmethod (9869122) memh5: modify BasicCont.redistribute to prevent reference cycles (0b7efaa) memh5: pass on detect_subclass and warn when axis selections won't work (f1d2e28) misc: change lock_file() behavior to match docstring (5aed55e) mpiarray: bug when selecting data during distributed read (3c5392a) mpiarray: upper limit incorrectly set in private function _reslice (4e2e1e4) setup: load install_requires from the requirements.txt file (9f25a57) time: allow zero length arrays as arguments (1a4b324) Features interferometry: add routines use for interferometry (2bf97e1) pfb: routines for calculating and correcting the effects of a PFB (a044741) pipeline: allow construction and control of pure Python pipelines (81af488) runner: support profiling caput-pipeline runs (9b72e7c) Performance Improvements weighted_median: use quickselect to calculate the static median (cfddefb)
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.440 | 0.398 |
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".