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Visions in quantum gravity

2025· article· en· W4413501687 on OpenAlexafffund
Luca Buoninfante, Benjamin Knorr, K. Sravan Kumar, Alessia Platania, Damiano Anselmi, Ivano Basile, N. E. J. Bjerrum-Bohr, Robert Brandenberger, Mariana Carrillo González, Anne-Christine Davis, Bianca Dittrich, P. Di Vecchia, John F. Donoghue, Fay Dowker, G. Dvali, Astrid Eichhorn, Steven B. Giddings, Alessandra Gnecchi, Giulia Gubitosi, Lavinia Heisenberg, Рената Каллош, Alexey S. Koshelev, Stefano Liberati, R. Loll, Leonardo Modesto, Paulo Vargas Moniz, Daniele Oriti, Olga Papadoulaki, Jan M. Pawlowski, Roberto Percacci, Lesław Rachwał, Mairi Sakellariadou, Alberto Salvio, K.S. Stelle, Sumati Surya, A.A. Tseytlin, Neil Turok, Thomas Van Riet, R. P. Woodard

Bibliographic record

VenueSciPost Physics Community Reports · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNoncommutative and Quantum Gravity Theories
Canadian institutionsPerimeter InstituteMcGill University
FundersAgencia Estatal de InvestigaciónNordForskEuropean Cooperation in Science and TechnologyMinistry of Colleges and UniversitiesHeising-Simons FoundationInnovation, Science and Economic Development Canada
KeywordsVisionQuantum gravityQuantumTheoretical physicsGeodesyGeologyPhysicsSociologyQuantum mechanicsAnthropology

Abstract

fetched live from OpenAlex

To deepen our understanding of Quantum Gravity and its connections with black holes and cosmology, building a common language and exchanging ideas across different approaches is crucial. The Nordita Program “Quantum Gravity: From gravitational effective field theories to ultraviolet complete approaches” created a platform for extensive discussions, aimed at pinpointing both common grounds and sources of disagreements, with the hope of generating ideas and driving progress in the field. This contribution summarizes the twelve topical discussions held during the program and collects individual thoughts of speakers and panelists on the future of the field in light of these discussions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.331
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.315
Teacher spread0.299 · 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 teacher head, 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

Citations8
Published2025
Admission routes2
Has abstractyes

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