Bibliographic record
Abstract
EnsembleKalmanProcesses v1.1.6 Diff since v1.1.5 Merged pull requests: Fix a typo in darcy.md (#346) (@glwagner) remove positive definiteness constraints, allow user defined additive inflation (#360) (@odunbar) CompatHelper: bump compat for SCS to 2, (keep existing compat) (#361) (@github-actions[bot]) add Project.toml for Localization example (#362) (@odunbar) bugfix logpdf broadcasting (#364) (@odunbar) NICE sample-error correction (#367) (@odunbar) Add troubleshooting doc (#368) (@costachris) Add save_parameter_samples (#370) (@nefrathenrici) CompatHelper: add new compat entry for Interpolations at version 0.15, (keep existing compat) (#376) (@github-actions[bot]) CompatHelper: bump compat for Convex to 0.16, (keep existing compat) (#379) (@github-actions[bot]) Complete redesign of "Observations" object enabling introduction of minibatching (#384) (@odunbar) Update version to v1.1.6 (#388) (@odunbar) Closed issues: O3.7.3 Overcome precompiling every (julia) ensemble member on HPC (#331) No Project.toml for the Localization example (#358) Positive definite corrections in get_u_cov (#359) Remove broadcasting for Logpdf. (#363) O3.7.7 Design a user-friendly guide for configuring EnsembleKalmanProcess (#365) Make SECFisher more accurate (#366) Improve speed of SECNice (#372) Add convenient method for minibatching data (#382) Add ability to mutate key quantities such as the observation covariance matrix (#383) ETKI ignores timestepper (#385)
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.053 |
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".