{"id":"W3008633137","doi":"10.3390/universe6070097","title":"Tensor Network Renormalization with Fusion Charges—Applications to 3D Lattice Gauge Theory","year":2020,"lang":"en","type":"preprint","venue":"Universe","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Observable; Physics; Lattice (music); Basis (linear algebra); Renormalization group; Granularity; Gauge theory; Statistical physics; Quantum; Lattice field theory; Quantum entanglement; Fusion; Scaling; Theoretical physics; Mathematics; Computer science; Mathematical physics; Quantum mechanics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006522369,0.0004120005,0.0003959449,0.0007243204,0.0005953554,0.0008825626,0.0006431534,0.0006309235,0.002220697],"category_scores_gemma":[0.001971729,0.0001768675,0.000458505,0.0006633878,0.001291256,0.001867033,0.001098119,0.0007023749,0.0002555606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008188374,"about_ca_system_score_gemma":0.0005776566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333346,"about_ca_topic_score_gemma":0.001918911,"domain_scores_codex":[0.9998011,0.00008334503,0.000007582773,0.00002226611,0.00006471232,0.00002090788],"domain_scores_gemma":[0.9994838,0.0002016517,0.00006162451,0.00009926347,0.00008110415,0.00007245682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005260832,0.00005242016,0.000517443,0.00003818907,0.00001281674,0.00008278511,0.0001392393,0.1723017,0.007214327,0.7964284,0.0006867113,0.02247345],"study_design_scores_gemma":[0.000006432921,0.00001167007,0.0000947074,0.000003559395,0.000001578235,0.00002331526,0.00001816894,0.7808427,0.0009739964,0.2169787,0.001037552,0.000007498079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1804658,0.0003293833,0.8056493,0.0005265415,0.0001112363,0.00005920452,0.00009058607,0.0004860684,0.01228185],"genre_scores_gemma":[0.7326816,0.0003947342,0.2620485,0.00008615693,0.00007209832,0.00009398524,0.0000855486,0.0002456917,0.00429175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002333346,"threshold_uncertainty_score":0.007428944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398155721081237,"score_gpt":0.2331231446405829,"score_spread":0.2191415874297705,"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."}}