{"id":"W2193054720","doi":"10.1007/jhep07(2016)100","title":"Tensor networks from kinematic space","year":2016,"lang":"en","type":"article","venue":"Journal of High Energy Physics","topic":"Black Holes and Theoretical Physics","field":"Physics and Astronomy","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Institut Périmètre de physique théorique; Industry Canada; University of Edinburgh; Office of Science; U.S. Department of Energy; California Institute of Technology; Centre de Recherches Mathématiques; Princeton University; Universiteit van Amsterdam; Government of Canada; National Science Foundation","keywords":"Geodesic; Kinematics; Conformal field theory; Space (punctuation); Tensor (intrinsic definition); Representation (politics); Integral geometry; Conformal map; Field (mathematics); Point (geometry)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119148,0.0005908362,0.0003815066,0.002206553,0.00137104,0.003328603,0.000682851,0.0009507114,0.01071941],"category_scores_gemma":[0.003566647,0.0003319932,0.0006386252,0.001297452,0.002638625,0.008913903,0.002399691,0.001737086,0.001072398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361478,"about_ca_system_score_gemma":0.0005437402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001456594,"about_ca_topic_score_gemma":0.001201468,"domain_scores_codex":[0.9992445,0.0002473953,0.00005346906,0.0001681722,0.0001937473,0.0000927827],"domain_scores_gemma":[0.9981155,0.0005849688,0.0003065929,0.0004339814,0.0003480447,0.0002108847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002832976,0.000001837784,0.00005191298,0.000009177992,0.000001453707,0.00001367877,0.0000557778,0.0008078036,0.00009511242,0.9969619,0.0002469688,0.00175158],"study_design_scores_gemma":[0.000001500002,0.00000438775,0.00007377003,0.000009545127,0.000001908923,0.00002780904,0.00003215029,0.00754792,0.00007610028,0.9886371,0.003583783,0.000003939479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.187054,0.003443828,0.6205196,0.005061973,0.000498692,0.0001262276,0.001582624,0.0005914698,0.1811216],"genre_scores_gemma":[0.9002807,0.002148527,0.07530024,0.0005499267,0.0005318926,0.0001838668,0.001165191,0.0002055596,0.01963422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01071941,"threshold_uncertainty_score":0.03586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004972113131175392,"score_gpt":0.1967292250586314,"score_spread":0.191757111927456,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}