Bibliographic record
Abstract
EnsembleKalmanProcesses v2.6.0 Diff since v2.5.0 Minor changes New default variant of EKS (ALDI), EKI now has a pseudoinverse fallback, and adds sqrt(eps)*I to covariances to avoid more singular exceptions Adds utility to see UKI ensemble before creating an EKP object updated documentation for EKI and EKS, (clarifications and latex bugfixes) Scalar covariances are now interpreted as UniformScalings not 1x1 matrices == for DataContainers Merged pull requests: Bump actions/checkout from 4 to 5 (#515) (@dependabot[bot]) Fix links in visualize section (#518) (@ph-kev) Add ALDI as default form of EKS (#519) (@odunbar) bugfix docs image link (#520) (@odunbar) Add fallback for linear solve (#521) (@nefrathenrici) Add convenience to construct UKI initial ensemble before creating EKP (#524) (@odunbar) Add some more non-breaking improvements before new minor release (#525) (@odunbar) Update Project.toml to v2.6.0 (#526) (@odunbar) Closed issues: O3.7.1 Momentum-accelerated EKP (#321) EKS update does not match docs "ALDI" description (#375) Add GNKI to the toolkit (#404) O6.X AMIP-calibration-focused developments (#446) scalar interpreted as scalar*I, not as 1x1 matrix (#472) Improve plotting functionality (#476) Bug throwing error in 1D problems with prior constructor (#494) Add == for DataContainers (#510) Incorrect Latex equations for inversion docs. (#511) Formatting of links incorrect (#516) Add linear solve methods to hand singular matrices (#517) Utility to help create the UKI initial sigma ensemble (#523)
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.325 | 0.304 |
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".