Bibliographic record
Abstract
What's New? Python frontend improvements: More Python features are supported, such as return values, tuples, and numpy broadcasting. @dace.programs can now call other programs or SDFGs. AMD GPU (HIP) Support: AMD GPUs are now fully supported with HIP code generation. Easy-to-use transformation APIs: Apply transformation compositions with one call, enumerate subgraph matches manually, and many more functions now available as part of the dace API. See the new tutorial for examples. Faster code generation: Backends now generate lower-level code that is more compiler-friendly. Instrumentation interface: Setting the instrument property for SDFG nodes and states enables easy-to-use, localized performance reporting with timers, GPU events, and PAPI performance counters. DaCe VSCode plugin: Interactive SDFG viewer and optimizer as part of Visual Studio Code. Download the plugin here. Type inference and connector types: In addition to automatic type inference, connectors on nodes can now be defined with explicit types, giving more fine-grained control over type reinterpreting and vector types. Subgraph transformations: New transformation type that can work on arbitrary subgraphs. For example, fuse any computation within a state with SubgraphFusion. Persistent GPU kernel schedule: Launch persistent kernels with a change of a property! Proportion used of GPU multiprocessors is configurable. More transformations: Loop manipulation and other new transformations now available with DaCe. Some transformations (such as Vectorization) made more robust to corner cases. More tools: Use sdfgcc to quickly compile and optimize .sdfg files from the command line, generating header and library files. Great for interoperability and Makefiles. Short DaCe annotation: Data-centric functions can now be annotated with @dace. Many minor fixes and additions: More library nodes (such as einsum) and new properties added, enabling faster performance and more productive high-performance coding than ever.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.364 | 0.310 |
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".