Bibliographic record
Abstract
Oceananigans v0.57.3 Diff since v0.57.2 Preliminary implementation of ImmersedBoundaryGrid and GridFittedBoundary! Closed issues: show() for VerticallyStretchedRectilinearGrId (#1637) Docstrings for VerticallyStretchedRectilinearGrids aren't in the Docs (#1651) GKS not in proper state (#1657) Should examples/mpi_shallow_water_turbulence.jl be in examples? (#1658) Merged pull requests: Fix set! for cubed sphere reduced fields (#1622) (@ali-ramadhan) Fixes show for VerticallyStretchedRectilinearGrid on GPUs (#1638) (@glwagner) Prototyping Oceananigans.OutputReaders (#1641) (@ali-ramadhan) Extends getindex for Nothing locations to AbstractField (#1649) (@glwagner) Validate the convergence rates for advection (#1650) (@francispoulin) Adds Grids/vertically_stretched_rectilinear_grid.jl in Docs (#1652) (@navidcy) Deletes needless comma in getindex for 0D fields (#1653) (@glwagner) Basic ImmersedBoundaryGrid + GridFittedBoundary implementation (#1654) (@glwagner) Using Flat for shallow water benchmark (#1659) (@francispoulin) moving mpi example to validation folder (#1661) (@francispoulin) assert shallow water model is flat and add test (#1662) (@francispoulin) Bump to 0.57.3 (#1671) (@glwagner)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.326 | 0.327 |
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".