{"id":"W2316359013","doi":"10.1080/01966324.2007.10737689","title":"Bayesian Analysis of Dyadic Data","year":2007,"lang":"en","type":"article","venue":"American Journal of Mathematical and Management Sciences","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of Connecticut","keywords":"Computer science; Markov chain Monte Carlo; Missing data; Bayesian probability; Variety (cybernetics); Class (philosophy); Variable-order Bayesian network; Inference; Covariate; Data mining; Bayesian inference; Machine learning; Artificial intelligence; Econometrics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004572197,0.00008260256,0.0003875774,0.0005531721,0.00006649795,0.00006524505,0.001473133,0.00001095617,0.00001209893],"category_scores_gemma":[0.00005081628,0.00005292947,0.00007679456,0.00188149,0.0005657627,0.0004073641,0.0003987645,0.00006011258,6.603075e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000569236,"about_ca_system_score_gemma":0.00001671369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004564406,"about_ca_topic_score_gemma":0.000002448078,"domain_scores_codex":[0.9985569,0.00006260297,0.000485149,0.0002175586,0.0004820868,0.0001957325],"domain_scores_gemma":[0.9987208,0.0002728824,0.0004441429,0.0003877321,0.00004493095,0.0001295028],"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":[0.000004993916,0.0000766298,0.000312983,0.00002845381,0.0003026895,0.00002297541,0.0002978828,0.00001454849,0.00002373671,0.5430031,0.00009872345,0.4558132],"study_design_scores_gemma":[0.0006015764,0.00176144,0.01974218,0.0002760834,0.002316486,0.000182392,0.002235011,0.3151219,0.0003327049,0.6552508,0.001606216,0.0005732757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008642484,0.0001138197,0.9852417,0.0006983883,0.00004133222,0.0000437059,8.443738e-7,0.00000625663,0.005211546],"genre_scores_gemma":[0.379065,0.00009585144,0.6206568,0.0001429353,0.00001078971,1.569235e-7,1.098204e-7,0.000001264864,0.00002714111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.45524,"threshold_uncertainty_score":0.2737472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373646764183357,"score_gpt":0.3319757045146938,"score_spread":0.2982392368728602,"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."}}