{"id":"W2953604448","doi":"","title":"The influence of learning algorithms for Bayesian Networks on predictions: A citation analysis study case","year":2019,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Bayesian probability; Bayesian network; Machine learning; Artificial intelligence; Cluster analysis; Variable-order Bayesian network; Algorithm; Data mining; Bayesian inference","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.1152913,0.001083888,0.00131296,0.003734737,0.002454959,0.007339748,0.002536442,0.004078374,0.0045657],"category_scores_gemma":[0.4050518,0.0005650287,0.001076907,0.004455301,0.003282916,0.01441355,0.002571219,0.006251358,0.001006042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005274245,"about_ca_system_score_gemma":0.002553998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01093069,"about_ca_topic_score_gemma":0.006993747,"domain_scores_codex":[0.9469799,0.04419937,0.00106814,0.002377235,0.004551244,0.0008240637],"domain_scores_gemma":[0.3688218,0.6046153,0.007044834,0.006393882,0.01186875,0.001255384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001390607,0.0009440843,0.08754572,0.0005761987,0.0003600078,0.000781606,0.005999307,0.2927567,0.0004887237,0.3495694,0.01162079,0.2479669],"study_design_scores_gemma":[0.0001496266,0.0002503961,0.006506299,0.0002483026,0.0001241853,0.000202794,0.001703626,0.7532964,0.001145267,0.2306223,0.005683403,0.00006750186],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5269113,0.005658963,0.3719236,0.02771767,0.0003308857,0.0007985582,0.0007794123,0.000770904,0.06510875],"genre_scores_gemma":[0.9396658,0.001108381,0.05603762,0.0005200347,0.0001733999,0.0002213035,0.0003032641,0.0001601779,0.001809966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9962653,"threshold_uncertainty_score":0.6097262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858189046233976,"score_gpt":0.2980044318096698,"score_spread":0.26942254134733,"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."}}