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Record W2060415987 · doi:10.1142/s0217751x08038640

TENSOR SELF-ENERGY IN A VECTOR-TENSOR MODEL

2008· article· en· W2060415987 on OpenAlexaff
Alex Buchel, Farrukh Chishtie, Marie Gagne-Portelance, Sirous Homayouni, D. G. C. McKeon

Bibliographic record

VenueInternational Journal of Modern Physics A · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Physical and Chemical Molecular Interactions
Canadian institutionsPerimeter InstituteWestern University
Fundersnot available
KeywordsPhysicsTensor fieldTensor (intrinsic definition)Tensor contractionTensor densityLanczos tensorStress–energy tensorEinstein tensorSymmetric tensorTensor product of Hilbert spacesAction (physics)Cartesian tensorMathematical physicsDegrees of freedom (physics and chemistry)Classical mechanicsField (mathematics)Exact solutions in general relativityRiemann curvature tensorQuantum mechanicsPure mathematicsGeometry

Abstract

fetched live from OpenAlex

The tensor self-energy is computed at one-loop order in a model in which a vector and tensor interact in a way that eliminates all tensor degrees of freedom. Divergencies arise which cannot be eliminated without introducing a kinetic term for the tensor field which does not appear in the classical action. A field redefinition serves to eliminate these divergences.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2008
Admission routes1
Has abstractyes

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