{"id":"W3004464990","doi":"10.3982/te3273","title":"Locally Bayesian learning in networks","year":2020,"lang":"en","type":"article","venue":"Theoretical Economics","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Bayesian network; Artificial intelligence; Tree (set theory); Variable-order Bayesian network; Machine learning; Bayesian probability; Bayesian inference; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005853551,0.0009334823,0.001831984,0.001696484,0.0009310299,0.003035745,0.002349716,0.00305278,0.003908414],"category_scores_gemma":[0.03825197,0.0009116195,0.0009344332,0.001633943,0.003511984,0.007541853,0.00244512,0.003360752,0.0006819255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003226349,"about_ca_system_score_gemma":0.001223078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237049,"about_ca_topic_score_gemma":0.004876156,"domain_scores_codex":[0.9959617,0.002141253,0.0001712772,0.0007642214,0.0006832012,0.0002783097],"domain_scores_gemma":[0.9733896,0.02247494,0.00135878,0.001102707,0.001177281,0.0004967167],"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":[0.00008621421,0.00004662792,0.001834227,0.0001438629,0.00007738783,0.00009431405,0.0002565507,0.3662153,0.000366245,0.5942604,0.002412838,0.034206],"study_design_scores_gemma":[0.00001806097,0.00001221036,0.0001734457,0.00001667066,0.000007841882,0.00001587195,0.00001579277,0.493461,0.0001089845,0.5052366,0.0009227438,0.00001073467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02378615,0.001009472,0.965555,0.002362063,0.00005859156,0.00006604593,0.0002777732,0.0002767578,0.006608238],"genre_scores_gemma":[0.7894026,0.002324056,0.1969613,0.0007523475,0.0004664943,0.0004836838,0.0006383257,0.0001533995,0.008817834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006237049,"threshold_uncertainty_score":0.03095686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005162024430970209,"score_gpt":0.2141595323974504,"score_spread":0.2089975079664802,"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."}}