Bibliographic record
Abstract
RavenR version 2.1.4 now on CRAN and released here. 2.1.4 Updates to a number of functions and new features implemented, including: removal of all dependencies on spatial packages (e.g. sf, raster), and removal of the netcdf-related functions; rvn_download and rvn_run to enable downloading and running Raven.exe within R; rvn_rvi_write_template to write model rvi files from templates in the Raven manual; rvn_budyko_plot to generate a budyko curve from model precip, AET, and PET; updates to rvi mapping, including improvements to add functionality from ggrepel library in spacing labels, and addition of the DiagrammeR library to support rvn_rvi_process_diagrammer; rvn_budyko_plot to generate a budyko curve from model precip, AET, and PET; updates to handling rvt reading and writing, which is now generic for all rvt types; and rvn_met_interpolate for performing inverse distance weighting interpolation to fill missing data values in meteorological data (works immediately with weathercan downloads).
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.270 | 0.443 |
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".