Bibliographic record
Abstract
The Hadamard renormalization procedure is applied to a free, massive Dirac field $\ensuremath{\psi}$ on a two-dimensional Lorentzian spacetime. This yields the state-independent divergent terms in the Hadamard bispinor ${G}^{(1)}(x,{x}^{\ensuremath{'}})=\frac{1}{2}⟨[\overline{\ensuremath{\psi}}({x}^{\ensuremath{'}}),\ensuremath{\psi}(x)]⟩$ as $x$ and ${x}^{\ensuremath{'}}$ are brought together along the unique geodesic connecting them. Subtracting these divergent terms within the limit assigns ${G}^{(1)}(x,{x}^{\ensuremath{'}})$, and thus any operator expressed in terms of it, a finite value at the coincident point ${x}^{\ensuremath{'}}=x$. In this limit, one obtains a quadratic operator instead of a bispinor. The procedure is thus used to assign finite values to various quadratic operators, including the stress-energy tensor. Results are presented covariantly, in a conformally flat coordinate chart at purely spatial separations, and in the Minkowski metric. These terms can be directly subtracted from combinations of ${G}^{(1)}(x,{x}^{\ensuremath{'}})$---themselves obtained, for example, from a numerical simulation---to obtain finite expectation values defined in the continuum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".