Bibliographic record
Abstract
Turing v0.36.3 Diff since v0.36.2 Merged pull requests: Remove selector/space stuff (#2458) (@mhauru) Return NaN for negative ModeResult variance estimates (#2471) (@frankier) Pin AdvancedPS test dep to 0.6.0 (#2482) (@penelopeysm) Documentation and Turing Navigation CI improvement (#2484) (@shravanngoswamii) Fixing doc import by prefixing DynamicPPL to predict (#2489) (@sunxd3) remove LogDensityProblemsAD (#2490) (@penelopeysm) more test fixes (#2491) (@penelopeysm) Remove x86 CI (#2495) (@penelopeysm) Simplify tests (#2496) (@penelopeysm) Make Gibbs work with step_warmup (#2502) (@mhauru) Closed issues: @model treats an observed variable as a random variable if it's provided by unpacking (#1978) Adding new issues and PRs to Project Board automatically. (#2315) ForwardDiff Optimization test failing (#2369) test/mcmc/Inference.jl segfaults on GHA Windows runner (sometimes) (#2379) filldist/arraydist construct NIW prior? (#2391) Performance hints (#2416) JuliaBUGS Meta Issue (#2435) Keeping a nice changelog (#2463) master -> main (#2479) How important is 32-bit testing? (#2486)
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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.745 | 0.834 |
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".