{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005669379,0.0009438148,0.001102215,0.00298286,0.0008655904,0.003022478,0.001952622,0.001961174,0.003944485],"category_scores_gemma":[0.04253978,0.001134886,0.001352475,0.003027966,0.002011792,0.005755903,0.002339373,0.002878252,0.0005018314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003192647,"about_ca_system_score_gemma":0.001829267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01424507,"about_ca_topic_score_gemma":0.01416996,"domain_scores_codex":[0.9962056,0.001882965,0.0002347209,0.0008258372,0.0006732956,0.0001776165],"domain_scores_gemma":[0.9709838,0.02425988,0.001811568,0.001339411,0.00122239,0.0003827948],"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":[0.00007542488,0.00003456227,0.002357677,0.0001776339,0.00008361377,0.0001799042,0.0003603814,0.3031738,0.000405743,0.6518721,0.002060101,0.03921913],"study_design_scores_gemma":[0.00001167199,0.000007087952,0.0002668075,0.00004625528,0.00002215083,0.0000405709,0.00002495563,0.410889,0.0001457905,0.5861677,0.00236196,0.00001598668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009457052,0.0005515136,0.9861417,0.0008228712,0.00003611697,0.00003224868,0.0007675963,0.0002559352,0.00193484],"genre_scores_gemma":[0.5514038,0.002575028,0.4373493,0.0006858996,0.0002927599,0.0004427411,0.00320297,0.0002508719,0.00379659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01424507,"threshold_uncertainty_score":0.02998292,"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."}}