{"id":"W3094210543","doi":"10.1002/cjs.11573","title":"Local structure graph models with higher‐order dependence","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sandia National Laboratories; U.S. Department of Energy; National Nuclear Security Administration; National Science Foundation","keywords":"Exponential random graph models; Random graph; Marginal distribution; Markov chain; Graph; Covariate; Mathematics; Mixed graph; Exponential family; Random field; Markov random field; Random variable; Statistical physics; Computer science; Applied mathematics; Discrete mathematics; Econometrics; Statistics; Line graph; Artificial intelligence; Voltage graph","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003772105,0.0001235693,0.000221535,0.00008058098,0.00008423478,0.00006195936,0.00023289,0.0000247543,0.001154437],"category_scores_gemma":[0.000003829883,0.000103994,0.0000413375,0.0002826,0.0001095843,0.0001103081,0.000008899127,0.000292524,0.00000195165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003611514,"about_ca_system_score_gemma":0.0006843196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00246906,"about_ca_topic_score_gemma":0.00690414,"domain_scores_codex":[0.9992331,0.00002504641,0.0002446683,0.0001053455,0.000171506,0.0002203907],"domain_scores_gemma":[0.9987285,0.00003201127,0.0001891013,0.00009981859,0.0003281774,0.0006223567],"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":[0.00005889531,0.00002011978,0.02753063,0.00002940436,0.000685045,0.0005876613,0.001025147,0.1331151,0.00006030684,0.6961146,0.09358628,0.04718682],"study_design_scores_gemma":[0.001448057,0.0008676377,0.005528776,0.000162935,0.0007198459,0.00007104828,0.0009833553,0.09820961,0.0004104603,0.859706,0.03081398,0.001078319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003534404,0.00008266532,0.9947463,0.0004085891,0.00004127299,0.00004267533,0.0003879299,0.000005278102,0.0007508712],"genre_scores_gemma":[0.9240579,0.00000176633,0.07545606,0.0002297844,0.0001936827,4.609973e-7,0.0000190737,0.00001593004,0.00002532164],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9205235,"threshold_uncertainty_score":0.9997587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01500258191161636,"score_gpt":0.2112016067583057,"score_spread":0.1961990248466894,"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."}}