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Record W4224211101 · doi:10.1088/1751-8121/ac8380

The SAGEX review on scattering amplitudes*

2022· article· en· W4224211101 on OpenAlexaff
Gabriele Travaglini, Andreas Brandhuber, Patrick Dorey, Tristan McLoughlin, Samuel Abreu, Zvi Bern, N. E. J. Bjerrum-Bohr, J. Blümlein, Ruth Britto, John Joseph M. Carrasco, Dmitry Chicherin, Marco Chiodaroli, P.H. Damgaard, Vittorio Del Duca, Lance J. Dixon, Daniele Dorigoni, Claude Duhr, Yvonne Geyer, Michael Green, Enrico Herrmann, Paul Heslop, Henrik Johansson, G.P. Korchemsky, David A. Kosower, Lionel Mason, Ricardo Monteiro, Donal O’Connell, Γεώργιος Παπαθανασίου, Ludovic Planté, Jan Plefka, Andrea Puhm, Ana-Maria Raclariu, Radu Roiban, Carsten Schneider, Jaroslav Trnka, Pierre Vanhove, Congkao Wen, Chris D. White

Bibliographic record

VenueJournal of Physics A Mathematical and Theoretical · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsPerimeter Institute
FundersEngineering and Physical Sciences Research CouncilHorizon 2020 Framework ProgrammeScience and Technology Facilities CouncilAgence Nationale de la RechercheEuropean Commission
KeywordsSuperstring theoryScattering amplitudePhysicsGreen–Schwarz mechanismScatteringTheoretical physicsField (mathematics)AmplitudeSeries (stratigraphy)Field theory (psychology)Quantum electrodynamicsMathematical physicsQuantum gravityQuantum mechanicsMathematicsSupersymmetryGeologyRelationship between string theory and quantum field theory

Abstract

fetched live from OpenAlex

Abstract This is an introduction to, and invitation to read, a series of review articles on scattering amplitudes in gauge theory, gravity, and superstring theory. Our aim is to provide an overview of the field, from basic aspects to a selection of current (2022) research and developments.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.004

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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
GenreReview

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

Citations83
Published2022
Admission routes1
Has abstractyes

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