{"id":"W6999162939","doi":"","title":"Characterizing Qualitative Causal Dependency for NAT-modeled Bayesian Networks","year":2023,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dependency (UML); Bayesian network; Bayesian probability; Nat; Focus (optics); Conditional probability; Subspace topology; Dependency theory (database theory); Tree (set theory); Conditional dependence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008604574,0.0002959294,0.0004735695,0.000215751,0.0005217952,0.00007814851,0.002035717,0.0003473204,0.00001474641],"category_scores_gemma":[0.00005485864,0.0003011606,0.0003131877,0.0005386082,0.00007212075,0.0003963442,0.0001959736,0.0004647763,0.00003362699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006953399,"about_ca_system_score_gemma":0.0002602877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006835199,"about_ca_topic_score_gemma":0.0007550981,"domain_scores_codex":[0.9981483,0.0002209317,0.0002690615,0.0005633612,0.0003713359,0.0004269962],"domain_scores_gemma":[0.9978543,0.0004753979,0.0005052409,0.0006585422,0.0004008457,0.0001056583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002010365,0.0004178688,0.0000414299,0.001994072,0.002030703,0.0001799526,0.5527369,0.007312127,0.05583473,0.2370843,0.01155185,0.1288057],"study_design_scores_gemma":[0.001188104,0.0003960649,0.0043189,0.0005369433,0.0003897711,0.000007716195,0.05368579,0.8970518,0.0002815523,0.04058811,0.0003614705,0.001193742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03874667,0.0002147171,0.9574553,0.0009622264,0.001073837,0.0004940116,0.00004555419,0.000261676,0.0007459647],"genre_scores_gemma":[0.9846926,0.0001797929,0.007625004,0.00005317792,0.0001292128,0.00000334849,0.0003541038,0.00003558292,0.006927188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9498304,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03685321737180531,"score_gpt":0.2870565959161419,"score_spread":0.2502033785443366,"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."}}