{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001868794,0.0004351997,0.0004223799,0.0002802423,0.0003055951,0.00021683,0.001833175,0.0004009785,0.000001603182],"category_scores_gemma":[0.00002218522,0.0005390854,0.0002443162,0.00002799978,0.0001374044,0.0002637689,0.00133589,0.0006948552,0.0002862361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722436,"about_ca_system_score_gemma":0.0001771072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004663858,"about_ca_topic_score_gemma":0.000004087454,"domain_scores_codex":[0.99795,0.0000424644,0.0002466001,0.0008900176,0.0004001406,0.0004708031],"domain_scores_gemma":[0.9981106,0.000199117,0.0002277527,0.001035135,0.0001775688,0.0002498602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008422694,0.000003314505,0.00000138532,0.00005276545,0.00006344456,0.0004701185,0.00006208733,0.00003951704,0.00001493407,0.9807669,0.0116833,0.006833843],"study_design_scores_gemma":[0.000486073,0.00008068668,0.00001275466,0.0007004006,0.0001300915,0.00003609194,0.00001287754,0.05913595,0.0001967894,0.000823809,0.9370934,0.001291123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000009123695,0.00006635887,0.3324584,0.00002790218,0.0003827946,0.0001335916,0.00001930877,0.0006633662,0.6662391],"genre_scores_gemma":[0.001133919,0.0001184265,0.003121459,0.000152302,0.0001073068,6.893947e-7,0.00001431701,0.00005710213,0.9952945],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.979943,"threshold_uncertainty_score":0.9997061,"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."}}