Bibliographic record
Abstract
Release Notes This second release candidate for 0.0.10 includes a major overhaul of the Neurosynth fetching and conversion functions. The Neurosynth database now follows a very different file format, in order to match NeuroQuery's convention. We also have a new function to fetch NeuroQuery, and the Neurosynth conversion functions will work with NeuroQuery data as well. Changes [ENH] Support new format for Neurosynth and NeuroQuery data (#535) @tsalo [DOC] Update citation for Enge et al. (2021) (#549) @alexenge [FIX] Use resample=True in IBMA examples (#546) @tsalo [FIX] Extract relevant metadata in kernel transformers for Dataset-based transform calls (#548) @tsalo [DOC] Update ecosystem figure and documentation (#545) @tsalo [ENH] Do not apply IBMA methods to voxels with zeros or NaNs (#544) @tsalo [REF] Remove unused dependencies and unimplemented workflow (#541) @tsalo [DOC] Change napoleon settings (#540) @tsalo [ENH] Add ROI association decoder (#536) @tsalo [ENH] Add custom __repr__ methods (#538) @tsalo [FIX] Update CircleCI config to fix recent bug (#537) @tsalo [ENH] Replace low_memory with memory_limit and reduce memory bottlenecks (#520) @tsalo
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.545 | 0.605 |
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".