Bibliographic record
Abstract
Turing v0.9.0 Diff since v0.8.3 Closed issues: ERROR: LoadError: getϵ(adaptor::AdvancedHMC.Adaptation.NoAdaptation) is not implemented. (#1071) latex code / math broken on docs website (#1079) defining infinite mixture of dirichlets (#1080) Tutorial error (#1082) Dead link to compositional sampler paper (#1094) Code cells showing errors were thrown in linear regression example (#1096) DynamicHMC errors on 1.3 (#1098) Test glitch probot (#1107) Test glitch probot (#1108) Test glitch probot (#1109) Test glitch probot (#1110) Turing LoadError (#1122) LinearRegression Example Performance suggestion (#1123) Merged pull requests: VI & ADVI improvements (#902) (@torfjelde) Run CI tests on Julia 1.3 (#1067) (@devmotion) CompatHelper: bump compat for "SpecialFunctions" to "0.10" (#1072) (@github-actions[bot]) Use AdvancedMH (#1083) (@cpfiffer) Add method for pdf(::OrderedLogistic, x) (#1089) (@baggepinnen) More compatible and clearer baseurl inquiry (#1092) (@KDr2) fixes and improvements on CRP implementations (#1093) (@trappmartin) Install TagBot as a GitHub Action (#1097) (@JuliaTagBot) Fix errors with DynamicHMC (#1099) (@devmotion) Run benchmarks via github action (#1100) (@KDr2) Update guide.md (#1103) (@yebai) Remove state-saving by default (#1105) (@cpfiffer) Add GSoC blog post (#1106) (@cpfiffer) Update to new AbstractMCMC API (#1116) (@devmotion) Use reset_num_produce! (#1117) (@devmotion) Reduce benchmark related comment reply (#1119) (@KDr2) Add team page to the website (#1124) (@cpfiffer) Add static distributions idea to GSoC ideas (#1125) (@mohamed82008) Remove TrackedArray implementation and update DistributionsAD (#1128) (@devmotion)
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.613 | 0.738 |
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".