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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.381 | 0.351 |
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".