Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.144 | 0.025 |
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 it