Dynamo seeds from gravitational torsional anomalies and de Sitter magnetized metrics
Bibliographic record
Abstract
Recently gravitational and Nieh–Yan (NY) chiral anomalies have been obtained in Riemann–Cartan space–time (L.C. Garcia de Andrade. Class Quantum Grav. 38(6), 065005 (2021). doi: 10.1088/1361-6382/abd25f ), where electrodynamics is encoded in the metric geometry. In this paper we follow the same pathway by obtaining a class of deformed de Sitter metrics in teleparallelism. The existence of the unmagnetized de Sitter metric (DSMM) without axial anomalies is proved. Unified theories à la Einstein, Eddington, and Schroedinger, called modified de Sitter metrics, present some novel features. First, we show that a pure DSMM in T4 does not induce gravitational anomalies. This is a motivation to study modifications of DSMM. NY torsional anomaly in DSMM teleparallel T4 geometry is shown to vanish in all cases. Gravitational non-trivial anomalies are obtained from these metrics. Torsional anomaly, much used in condensed matter physics, does not vanish. From these deformed DSMM, we show that a dynamo equation with torsional gradient sources is valid from class III of the metrics but is torsionless sourced in class II. We show that in the gravitational anomaly of new deformed de Sitter metric one may cancel the gravitational anomaly, by a proper choice of the metric function. The axial anomaly is obtained for some metric deformation as well. A simple deformation leads to the existence of the NY density in the case of DSMM. This would be class IV of DSMM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".