{"id":"W4387592225","doi":"10.1017/9781316718636.018","title":"Going Wide, Going Deep","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Confounding; Econometrics; Data science; Artificial intelligence; Operations research; Economics; Mathematics; Statistics","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":[0.007623331,0.000756067,0.001086571,0.001642892,0.00166803,0.00443009,0.001544832,0.001878655,0.02225355],"category_scores_gemma":[0.02349658,0.0007414183,0.000777927,0.001823751,0.006787123,0.0155556,0.003575157,0.005962284,0.003856351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001614134,"about_ca_system_score_gemma":0.001584066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001174699,"about_ca_topic_score_gemma":0.003335737,"domain_scores_codex":[0.9979355,0.001039092,0.00007532366,0.0003816134,0.0003977757,0.0001706992],"domain_scores_gemma":[0.9855774,0.01165369,0.00038758,0.001257035,0.0004971906,0.0006270107],"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":[0.0001454787,0.00008135631,0.002075207,0.0004114624,0.00006193537,0.0001713891,0.001993052,0.004215718,0.001294953,0.6780845,0.05380417,0.2576608],"study_design_scores_gemma":[0.00001486128,0.00005087968,0.000742524,0.0001783525,0.00002609316,0.0002465556,0.0005879158,0.003633281,0.0003296901,0.9271744,0.0669946,0.00002084009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0356325,0.03354878,0.4229843,0.06842762,0.002245181,0.0003290048,0.000946855,0.0008520005,0.4350339],"genre_scores_gemma":[0.4601721,0.02459991,0.3386637,0.03254231,0.003215705,0.0006093081,0.0009268103,0.001169145,0.138101],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02225355,"threshold_uncertainty_score":0.07444555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03594113853023687,"score_gpt":0.2065304893456798,"score_spread":0.170589350815443,"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."}}