{"id":"W2726536615","doi":"","title":"Extraction of NAT Causal Structures Based on Bipartition.","year":2017,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Nat; Computer science; Extraction (chemistry); Artificial intelligence; Computer network; Chemistry; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004196605,0.001089697,0.001403296,0.00861877,0.002265843,0.002868996,0.001570184,0.001852668,0.01407712],"category_scores_gemma":[0.03585682,0.0009898084,0.00244725,0.006761849,0.001244308,0.003912734,0.002913126,0.002607905,0.004599751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299661,"about_ca_system_score_gemma":0.003900563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006695822,"about_ca_topic_score_gemma":0.01269069,"domain_scores_codex":[0.9960382,0.001706723,0.0003180517,0.0009256039,0.0008195862,0.000191758],"domain_scores_gemma":[0.9772999,0.01600035,0.001476663,0.002332904,0.002462036,0.0004280424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009603532,0.0002068919,0.01658634,0.002231267,0.0007316556,0.001672721,0.001372244,0.04459763,0.006237415,0.4591147,0.05663681,0.4096518],"study_design_scores_gemma":[0.00007770748,0.00005746459,0.004028857,0.0005950927,0.0004043191,0.0006557261,0.0002956999,0.1994509,0.004219455,0.7437806,0.04634532,0.00008891722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008670051,0.001656289,0.9675223,0.0009307681,0.000226127,0.0002037735,0.008783421,0.002112343,0.009895033],"genre_scores_gemma":[0.2877749,0.001850055,0.6807536,0.0005336456,0.0003711147,0.0006939478,0.02227335,0.000690543,0.005058844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01407712,"threshold_uncertainty_score":0.04709268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1285084442485812,"score_gpt":0.4305560010082635,"score_spread":0.3020475567596823,"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."}}