{"id":"W2144782648","doi":"10.1162/neco_a_00603","title":"Intrinsic Graph Structure Estimation Using Graph Laplacian","year":2014,"lang":"en","type":"letter","venue":"Neural Computation","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Research Institute for Sustainable Humanosphere, Kyoto University","keywords":"Mixed graph; Mathematics; Laplacian matrix; Spurious relationship; Graph; Line graph; Null graph; Covariance matrix; Algorithm; Strength of a graph; Voltage graph; Theoretical computer science; Computer science; Discrete mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007266736,0.0004019863,0.0004619572,0.0004214155,0.0002078181,0.0001971238,0.0002353566,0.0002467145,0.0001104839],"category_scores_gemma":[0.000003696397,0.0004097569,0.0002775262,0.0005009868,0.00005506824,0.0001675876,0.00006241461,0.001091308,0.000008232928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005107828,"about_ca_system_score_gemma":0.00002678027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002168966,"about_ca_topic_score_gemma":0.000006944421,"domain_scores_codex":[0.9982048,0.0002098493,0.0004349132,0.0004820366,0.0003506312,0.0003177273],"domain_scores_gemma":[0.9988788,0.000105042,0.000507995,0.0002956269,0.0001712748,0.00004124144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001320451,0.00002412285,0.002158246,0.0001162356,0.000267213,0.00001741152,0.00009765053,0.1542089,0.000177219,0.0006192696,0.6975978,0.1447027],"study_design_scores_gemma":[0.0003046731,0.00006111512,0.0004645273,0.00009338692,0.0003454155,0.00001021094,0.000006008456,0.851706,0.0001573171,0.1301851,0.01593976,0.0007264411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08841237,0.00004373759,0.8770858,0.03189131,0.0007024935,0.0007511114,0.000077532,0.0004872738,0.0005483534],"genre_scores_gemma":[0.9424814,3.88656e-7,0.01146752,0.03790769,0.003857235,0.00001084965,0.004180245,0.00007097398,0.00002371938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8656183,"threshold_uncertainty_score":0.9998354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927107156380969,"score_gpt":0.2770519465576924,"score_spread":0.2577808749938827,"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."}}