Pyami: a python wrapper for the libami \nlibrary
Bibliographic record
Abstract
We present pyami, a Python library to evaluate the frequency integrals encountered \nin the evaluation of Feynman diagrams. pyami is code bindings for the C++ library \nlibami, which implements the Algorithmic Matsubara Integration technique that has \nbeen proposed in recent years. By implementing this library into Python, the plethora \nof mathematical Python libraries are now at one's disposal to evaluate the remaining \nspatial momentum integrals after the algorithmic Matsubara integration process. \nOnce provided the topologies of the Feynman diagrams of interest, the values can \nbe computed within an interactive Python environment such as a Jupyter Notebook. \nWe then show example calculations using the Python importance sampling package, \nVEGAS, to evaluate self-energy diagrams on the real frequency axis by a renormalized \nperturbation theory scheme described in our recent work.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.150 | 0.127 |
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".