{"id":"W3083631887","doi":"10.48550/arxiv.2009.02294","title":"Chordal Decomposition for Spectral Coarsening","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Solver; Operator (biology); Computation; Regular polygon; Decomposition; Algorithm; Computer science; Chordal graph; Mathematical optimization; Mathematics; Visualization; Theoretical computer science; Geometry; Graph; Artificial intelligence","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":[0.0004661277,0.0008074825,0.0005520394,0.0004934989,0.0004685308,0.001075861,0.000895881,0.000844004,0.007746786],"category_scores_gemma":[0.002130359,0.0002862994,0.0006140589,0.0004667486,0.0009026289,0.0009928033,0.001846355,0.001809324,0.001876877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005740875,"about_ca_system_score_gemma":0.0008036003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806678,"about_ca_topic_score_gemma":0.002841223,"domain_scores_codex":[0.9996302,0.00006542556,0.00002010104,0.00005972702,0.0001867743,0.00003775679],"domain_scores_gemma":[0.9995396,0.0001770676,0.00003445702,0.0001121956,0.00009330638,0.00004340963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001377709,0.0001014622,0.0007918204,0.0002946004,0.00006110092,0.0001873735,0.000285608,0.4490707,0.0459877,0.2982979,0.0137738,0.1910102],"study_design_scores_gemma":[0.00001594502,0.00001665141,0.00004572242,0.00001636948,0.000005161153,0.00004831231,0.00002093269,0.93541,0.005145183,0.04755812,0.01170747,0.00001013657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002502226,0.00008909106,0.9930509,0.00009698376,0.00005828621,0.00002259361,0.0000499747,0.0003022602,0.003827689],"genre_scores_gemma":[0.1367554,0.0002604353,0.855696,0.0002322365,0.0001023989,0.0001544691,0.000262316,0.000812554,0.005724119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007746786,"threshold_uncertainty_score":0.02591556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09391958014620855,"score_gpt":0.2450668959141029,"score_spread":0.1511473157678943,"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."}}