Bibliographic record
Abstract
What's Changed 🎉 Exciting New Features Remove resample argument from IBMA estimators by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/823 Add IBMAWorkflow by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/817 Make torch optional by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/836 ### 🐛 Bug Fixes Addresses new RTD configuration file requirements by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/829 ### Other Changes Fix the NeuroLibre badge by @tsalo in https://github.com/neurostuff/NiMARE/pull/824 [FIX] handle null values in metadata by @jdkent in https://github.com/neurostuff/NiMARE/pull/831 Add badges and citations for Aperture Neuro article by @tsalo in https://github.com/neurostuff/NiMARE/pull/834 Remove pytorch warning message by @yifan0330 in https://github.com/neurostuff/NiMARE/pull/828 [FIX] handle index errors by @jdkent in https://github.com/neurostuff/NiMARE/pull/839 Full Changelog: https://github.com/neurostuff/NiMARE/compare/0.2.0rc2...0.2.0rc3
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.644 | 0.740 |
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".