neurostuff/NiMARE: 0.0.8
Bibliographic record
Abstract
Release Notes This release includes a number of bug-fixes, along with enhancements to how many tools within NiMARE implement low-memory options. In addition, we have renamed the CBMA estimators' null methods. The "analytic" method is now "approximate" and the "empirical" method is now "montecarlo". Changes [REF] Rename CBMA null distribution generation methods (#494) @tsalo [FIX] Add informative error when NeuroVault collection is not found (#500) @tsalo [ENH] Support symmetric GCLDA topics with more than two subregions (#499) @tsalo [DOC] Add sphinx-copybutton to docs requirements (#502) @tsalo [ENH] Incorporate information about valid masking approaches into IBMA Estimators (#495) @tsalo [FIX] Deal with extreme t-values in t_to_z by truncating associated p-values (#498) @tsalo [TST] Add flake8-isort to test dependencies (#493) @tsalo [REF] Miscellaneous GCLDA cleanup (#486) @tsalo [DOC] Add new functions and classes to API documentation (#490) @tsalo [ENH] add images_to_coordinates (#446) @jdkent [ENH] Add check_type function (#480) @tsalo [REF] Add low_memory option to Estimators and add function for moving metadata from Dataset to DataFrame (#476) @tsalo [FIX] Set Dataset.basepath using absolute path (#474) @tsalo [FIX] Find common stem in find_stem instead of largest common substring (#472) @tsalo [FIX] Replace misspelled "log_p" with "logp" (#468) @tsalo [FIX] Assume non-symmetric null distribution in ALESubtraction (#464) @tsalo [TST] Add memmap test. (#463) @jdkent [REF] Write temporary files to the NiMARE data directory (#460) @tsalo [REF] Use saved MA maps, when available, in CBMA estimators (#462) @tsalo [FIX] Neurovault name collisions (#457) @jdkent [FIX] Update niftimasker in dataset blob (#459) @jdkent [FIX] Add work-around for maskers that do not accept 1D input (#455) @jdkent [ENH] Add low-memory option for kernel transformers (#453) @tsalo [ENH] add function to convert neurovault collections to a NiMARE dataset (#432) @jdkent [FIX] Ensure IBMA results have the expected number of dimensions (#450) @jdkent [STY, TST] Add flake8-docstrings to requirements (#435) @tsalo
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.831 | 0.812 |
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".