{"id":"W7112387849","doi":"","title":"GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure","year":2023,"lang":"","type":"article","venue":"Lirias (KU Leuven)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; AstraZeneca; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Generative grammar; Relational database; Feature (linguistics); Generative model; Frame (networking)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001231134,0.0005804562,0.0005572845,0.00021796,0.00123053,0.0007428491,0.002410898,0.0005134927,0.0001323916],"category_scores_gemma":[0.0002929826,0.0006233601,0.0001545593,0.001076053,0.0001488249,0.001750665,0.001255201,0.001165231,0.0002863554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142341,"about_ca_system_score_gemma":0.0005854158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000612915,"about_ca_topic_score_gemma":0.00001019439,"domain_scores_codex":[0.9949019,0.000334867,0.0008585936,0.001985195,0.0008501781,0.001069242],"domain_scores_gemma":[0.9966757,0.000555054,0.0004042959,0.001657773,0.0003905671,0.0003166414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003648505,0.00003876369,0.0000452919,0.00006346378,0.0001248338,0.00001176774,0.001919213,0.900022,0.001246193,0.03769683,0.03396311,0.024832],"study_design_scores_gemma":[0.0006463229,0.0001644217,0.00001064687,0.0001232486,0.00006283409,0.000007640192,0.000117689,0.923031,0.0003610275,0.02695419,0.04785584,0.0006650865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01174086,0.001733259,0.9783893,0.003925143,0.002057329,0.0005094672,0.001101219,0.0003832861,0.00016008],"genre_scores_gemma":[0.8254961,0.0004242983,0.1577113,0.000653917,0.001628753,0.00005089105,0.003875107,0.0001178655,0.01004178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8206781,"threshold_uncertainty_score":0.9996217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471887329351943,"score_gpt":0.3134259046445874,"score_spread":0.166237171709393,"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."}}