Bibliographic record
Abstract
We discuss the current status of our automatic perturbation theory program as applied to Fermilab Fermions. We give an overview of our methods, a discussion of tree level matching, and one loop results for the coefficients of the higher dimension kinetic operators. A dominant source of error in current lattice calculations is the errors due to finite lattice spacing a. One way to reduce these errors is to use improved actions. The general structure for an improved action is L = L0 + cn(g0, m0)Ln (1) n=0 The series here is an expansion in the dimensionality of the various operators. Each of these terms comes with a new coupling constant cn. In order to use this type of action we must do two things, we must truncate the series at some specified order in n and we must calculate the new couplings somehow. Fixing the new couplings can be done in a number of ways, for example, the first step could be to make an expansion in powers of m0 for light quarks, or powers of 1/m0 for heavy quarks. The remaining dependence on g0 can be determined perturbativly. It is desirable to have a method of improvement which does not rely on light or heavy quark mass expansions. One such approach is the Fermilab approach [1] which orders the expansion operators by dimension only (this amounts to a small momentum expansion). The unimproved fermilab action consists of dimension two and four operators, S0 = dx ¯ { 1 + γ0 ψ m0 + + ζγ · D − rsζ
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".