{"id":"W2569593167","doi":"10.3389/fenvs.2016.00084","title":"Encoding Dependence in Bayesian Causal Networks","year":2017,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Bayesian network; Covariate; Computer science; Spatial analysis; Conditional probability; Autocorrelation; Bayesian probability; Causal structure; Conditional probability distribution; Joint probability distribution; Markov chain; Mathematics; Artificial intelligence; Machine learning; Econometrics; Statistics","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":[0.001117208,0.0001885532,0.0002022652,0.00023569,0.0006155176,0.0005263824,0.003805374,0.00008936776,0.00001045526],"category_scores_gemma":[0.00006465519,0.0001939729,0.00003536241,0.0002992181,0.0009577802,0.002356362,0.0009617693,0.0003597536,0.0000140429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000423812,"about_ca_system_score_gemma":0.0000843426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000167763,"about_ca_topic_score_gemma":0.00008591565,"domain_scores_codex":[0.9975064,0.00004682639,0.0002861336,0.0008479292,0.0005537499,0.0007589573],"domain_scores_gemma":[0.9985664,0.00001965243,0.0001416318,0.001069793,0.000004671604,0.0001978818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009004837,0.0001266736,0.8550183,0.000003859168,0.000002315983,0.0001707781,0.0007234102,0.01291235,0.003182425,0.004434679,0.000119013,0.1232971],"study_design_scores_gemma":[0.0002392977,0.00003024214,0.2753367,0.00004306811,0.000001019303,0.00001237338,0.00009054737,0.7191049,0.0008284178,0.003989368,0.00005940381,0.0002645986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1107915,0.0001872167,0.8845465,0.0002263358,0.001210731,0.0001362298,0.000001077321,0.00003464323,0.002865713],"genre_scores_gemma":[0.9404828,0.0001379512,0.05912886,0.0001209875,0.00003282268,0.00001413098,4.895374e-7,0.000006805587,0.00007512708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8296913,"threshold_uncertainty_score":0.7909986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212331835478599,"score_gpt":0.2338515058858248,"score_spread":0.2217281875310388,"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."}}