Bibliographic record
Abstract
Release Notes This has been a busy month for NiBetaSeries. We have two more methods for calculating betas (LSA and FS), and LSS has been modified to account for separate conditions. All of this great work is thanks to @tsalo. The second major change is the refactor of how we read from the FMRIPREP directory, previously we assumed results from fmriprep version (< v1.2.0), but now we only support files output from fmriprep (>= v1.2.0). If you have results from an older version of fmriprep, check our FAQ for a potential solution. The third major change is the generation of a citation template, so you can easily populate your methods section with the appropriate information. Again, thanks to @tsalo for this marvelous contribution. The fourth and final major change (in no particular order), is passing the beta series image maps directly to the output directory, no longer requiring the user to have an atlas and a lookup table to use NiBetaSeries. This will allow users to use the beta series image maps for whatever downstream analysis they wish. Thank you to all the contributors mentioned below for improving NiBetaSeries through documentation fixes and other code changes. An unsung hero is @PeerHerholz for code review and beneficial recommendations for the future of NiBetaSeries, Thank you! Also not listed is @mwvoss for opening issue #123. Making a good issue is work and should be recognized, thank you! While I have almost certainly missed giving thanks to everyone that has helped, please know I appreciate your contributions and I'm thankful you took some time out of your day to help this project grow. CHANGES [REL] v0.4.0rc1 (#239) @jdkent [DOC] update instructions with template checklist (#242) @jdkent [FIX] update code-server version (#238) @jdkent [DOC] Generate citable boilerplates for workflows (#205) @tsalo [DOC] Clarify in demo that you are stripping color codes #123 (#234) @ipacheco-uy [DOC] Fix documentation headers (#235) @atrievel [FIX] add nano to dev container (#233) @pranesh-sp [DOC] add lsa section (#231) @jdkent [DOC] add joss badge (#229) @zkhan12 [ENH,DOC] add development documentation section (#222) @jdkent [DOC,FIX] add fake img and lut to participant workflow (#225) @jdkent [ENH] Implement finite BOLD response- separate (FS) modeling (#204) @tsalo [MAINT] allow more lenience for pull requests (#223) @jdkent [ENH] Make atlases optional (#213) @jdkent [FIX,DOC] make title for changelog (#221) @jdkent [MAINT] make travisci more efficient (#216) @jdkent [FIX] make codecov yaml valid (#220) @jdkent [FIX] show binder badge on readthedocs (#219) @jdkent [ENH,DOC] sphinx gallery binder (#217) @jdkent [MAINT] make codecov more lenient (#215) @jdkent [FIX] use scope=derivatives in collect_data (#212) @jdkent [FIX] respond to suggested edits (#206) @jdkent [ENH] Implement least squares- all (LSA) modeling (#202) @tsalo [TST] add more tests (#201) @jdkent [FIX, DOC] Rename low-pass filter to high-pass filter (#198) @tsalo [MAINT] explicitly set codecov settings (#200) @jdkent [ENH,FIX] refactor bids file processing (#193) @jdkent [ENH] Separate other conditions in LSS model (#191) @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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.543 | 0.654 |
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".