Bibliographic record
Abstract
Breaking Changes The default option "all" for the effects argument of find_parameters() and get_parameters() for models from package brms and rstanarm has a new behaviour and only returns fixed effects and random effects variance components, but no longer the group level estimates. Use effects = "full" to return all parameters. This change is mainly to be more flexible and gain more efficiency for models with many parameters and / or many posterior draws. New functions is_bayesian_model() as a convenient shortcut to check whether a model is Bayesian or not. Changes Revised wording for alerts from get_variance(). The effects argument of find_parameters() and get_parameters() for models from package brms and rstanarm get two new options, "grouplevel" and "random_variances", to return only random effects variance components, or group level effects. This is more efficient especially for models with many samples and many parameters. Additionally, a variable argument can be passed to get_parameters(), which is in turn passed to as.data.frame(), to extract parameters more efficiently. The by argument in export_table() now also splits tables when format is not "html". Bug fixes Fixed issue in find_formula() for models of class barts (package dbarts), when formula was abbreviated using y ~ ..
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.434 | 0.312 |
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; the direct Gemma label and the distilled Codex classifier 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".