Eikonal approximation in celestial CFT
Bibliographic record
Abstract
Agradecimentos: We are grateful to Tim Adamo, Sebastião Alves Dias, Brando Bellazzini, Vincent Chen, Laurent Freidel, Charles Marteau, Rob Myers, Dominik Neuenfeld, Monica Pate, João Paulo Pitelli Manoel and Pedro Vieira for useful conversations, as well as João Paulo Pitelli Manoel, Laurent Freidel, Dan Kapec and Andrew Strominger for comments on a draft. We also acknowledge an anonymous referee for several constructive comments. A.R. is supported by the Ptarmigan foundation through the Stephen Hawking fellowship. L.P. de Gioia is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, process number 140725/2019-9) and would like to thank Perimeter Institute for hospitality during his visit. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".