{"id":"W4313889524","doi":"10.1186/s13634-022-00960-6","title":"Autoregressive graph Volterra models and applications","year":2023,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Science Foundation","keywords":"Identifiability; Computer science; Autoregressive model; Graph; Theoretical computer science; Volterra series; Identification (biology); Artificial intelligence; Machine learning; Nonlinear system; Mathematics; Econometrics","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":[0.001149096,0.0006190001,0.0007668535,0.0009594452,0.0002778614,0.001057627,0.0009805257,0.00127576,0.002191956],"category_scores_gemma":[0.004910292,0.0003555613,0.0007932966,0.001325019,0.0006195877,0.0008899028,0.0007439435,0.001444565,0.0005147321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000761389,"about_ca_system_score_gemma":0.0005075021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008118555,"about_ca_topic_score_gemma":0.00468054,"domain_scores_codex":[0.9995494,0.0002175126,0.0000193818,0.00009348883,0.00008115791,0.00003891052],"domain_scores_gemma":[0.9980136,0.001514377,0.0001775514,0.00008542504,0.0001598448,0.00004916872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001817299,0.00003062631,0.001100578,0.00006331036,0.00005007002,0.00007764164,0.00005473384,0.8658373,0.0005160384,0.1039556,0.00172475,0.02657108],"study_design_scores_gemma":[0.000001016497,0.000002941323,0.0001101537,0.000004794858,0.000002557445,0.000006621588,0.000004415803,0.9753303,0.00003966714,0.02408651,0.0004079466,0.000003105492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0194911,0.001983112,0.9730709,0.001385936,0.0001274535,0.00002243112,0.0001841705,0.0003636136,0.003371158],"genre_scores_gemma":[0.8774852,0.0037521,0.109039,0.0003791793,0.0002775614,0.0001097064,0.000465021,0.000132527,0.00835984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008118555,"threshold_uncertainty_score":0.01614261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02070986338211435,"score_gpt":0.3212073614156174,"score_spread":0.3004974980335031,"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."}}