Bibliographic record
Abstract
What's Changed guard against empty shape arrays by setting initial value in reduce operations over state dimensions in State by @tclose in https://github.com/nipype/pydra/pull/818 add slurm dockerfile by @adi611 in https://github.com/nipype/pydra/pull/729 added guard against missing implicit helps in custom compose packages by @tclose in https://github.com/nipype/pydra/pull/819 doc: Use inner/outer splitter, analogize to zip/product by @effigies in https://github.com/nipype/pydra/pull/773 fixes ci-cd issues by @tclose in https://github.com/nipype/pydra/pull/832 Cli by @jadecci in https://github.com/nipype/pydra/pull/831 relaxed task definition validation to accept use cases found in ANTs, FSL, Freesurfer, etc... task packages by @tclose in https://github.com/nipype/pydra/pull/835 New Contributors @jadecci made their first contribution in https://github.com/nipype/pydra/pull/831 Full Changelog: https://github.com/nipype/pydra/compare/1.0a1...1.0a2
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; both teacher heads agree on what is shown here.
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".