Bibliographic record
Abstract
What's Changed 🛠 Breaking Changes Support clusters table in Diagnostics by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/765 ### 🎉 Exciting New Features Add save() and load() methods to MetaResult objects by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/771 Incorporate Estimator and Corrector descriptions into MetaResult objects by @tsalo in https://github.com/neurostuff/NiMARE/pull/724 Add cluster_threshold option to Diagnostics by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/777 Add CBMA workflow by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/761 [ENH] rudimentary support for nimads by @jdkent in https://github.com/neurostuff/NiMARE/pull/763 ### 🐛 Bug Fixes Do not zero out one-tailed z-statistics for p-values > 0.5 by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/693 ### Other Changes Support nibabel 5.0.0 by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/762 Link to NeuroStars software support category instead of neuro questions by @tsalo in https://github.com/neurostuff/NiMARE/pull/768 Revert "Do not zero out one-tailed z-statistics for p-values > 0.5" by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/769 [DOC] add note about SDM by @jdkent in https://github.com/neurostuff/NiMARE/pull/764 Replace pandas.DataFrame.append with pandas.concat by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/774 Major refactoring of Diagnostics module by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/776 [DOC] add proper documentation to nimads module by @jdkent in https://github.com/neurostuff/NiMARE/pull/778 Full Changelog: https://github.com/neurostuff/NiMARE/compare/0.0.13...0.0.14rc1
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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) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.366 | 0.531 |
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".