JSOSuite.jl: One stop solutions for all things optimization
Bibliographic record
Abstract
JSOSuite v0.2.0 Diff since v0.1.1 Merged pull requests: [documentation] Update Documenter.jl (#129) (@amontoison) Add tutorial for in-place solve (#134) (@tmigot) Fix bug in bounds selection (#137) (@tmigot) Add multi start strategy (#138) (@tmigot) Bump to NLPModels 0.21 (#139) (@tmigot) chore: Limit the number of problems in the longer tests to 5 (#145) (@abelsiqueira) Fix docs build error (#148) (@abelsiqueira) chore: 🤖 Update CompatHelper (#155) (@abelsiqueira) fix: 🐛 Limit version of OptimizationProblems to 0.7.3 (#158) (@abelsiqueira) refactor: 💡 Change optional args of select_optimizers to kwargs (#160) (@abelsiqueira) CompatHelper: bump compat for JSOSolvers to 0.12, (keep existing compat) (#164) (@github-actions[bot]) Update .cirrus.yml (#166) (@amontoison) CompatHelper: bump compat for ADNLPModels to 0.8, (keep existing compat) (#167) (@github-actions[bot]) Bump to OptimizationProblems 0.8 in tests (#168) (@tmigot) Clean BIB file (#169) (@tmigot) CompatHelper: bump compat for NLPModelsJuMP to 0.13, (keep existing compat) (#170) (@github-actions[bot]) Version 0.2.0 (#171) (@tmigot) Closed issues: Add documentation for in-place optimization (#117) Move bmark_solvers wrapper to SolverBenchmark.jl (#136) Fix documentation (#141) Add workflow dispatch trigger to ComptaHelper (#154) Limit version of OptimizatonProblems (#156) Update to OptimisationProblems 0.7.4 (#157) Refactor select_optimizers to have only the problem as positional (#159)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".