Bibliographic record
Abstract
This release introduces two breaking changes, necessitating a new major version: The deprecated KoopmanPipeline.predict_multistep() method has been removed. They kernel_or_ift parameter of RandomFourierKernelApprox has been renamed to kernel_or_ft, and the corresponding ift_ attribute has been renamed to ft_. Other than bug fixes, the most notable improvement is the significant reduction of import time, which is due to the removal of the pandas dependency. Full changelog: https://github.com/decargroup/pykoop/compare/v1.2.3...v2.0.0 New features Removed pandas dependency to resolve slow imports (#166) Bug fixes Fixed incorrect argument names for kernel approximation (#175) Fixed bug when using multioutput='raw_values' regression metric keyword argument when scoring (#164) Fixed prediction bug when no inputs are used (#173) Fixed scikit-learn method resolution order (#177) Fixed default LMI strictness (#168)
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.373 | 0.467 |
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".