CliMA/EnsembleKalmanProcesses.jl: v0.10.0
Bibliographic record
Abstract
EnsembleKalmanProcesses v0.10.0 Diff since v0.9.1 Closed issues: Small question: parallellization (#148) Fix warnings in building docs (#167) Update docs API with new base methods (#182) Overloading == for ParameterDistributions (#184) Unlink parameter types (#185) constrained_gaussian type constructor for marginal-defined multivariate distributions (#192) Take out file_parsing_uq utils from src (#194) Merged pull requests: add N_ens input to Unscented, it can be N+2 or 2N+1 (#163) (@Zhengyu-Huang) Bugfixes to Documenter config, docs links (#166) (@tsj5) Docs for data container and HPC (#179) (@odunbar) Unlink real inputs in ParameterDistributions. (#186) (@ilopezgp) Fixes CI to Julia 1.7.3, tests not yet working for 1.8 (#187) (@ilopezgp) Add localization documentation. (#189) (@ilopezgp) Improve API docs for Localizers. (#191) (@ilopezgp) Improve EKP testing (#193) (@ilopezgp) Take file_parsing_uq tools out of src. (#195) (@ilopezgp) ParameterDistributions.jl improvements (#196) (@odunbar) Improve and organize API, add lead developers, update citations. (#197) (@ilopezgp)
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.297 | 0.264 |
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".