{"id":"W4394629225","doi":"10.1109/csce60160.2023.00362","title":"Meta Generative Data Augmentation Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Hokkaido University","keywords":"Computer science; Generative grammar; Artificial intelligence","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":[0.001084785,0.001397367,0.001316266,0.0008609097,0.0003747219,0.001011198,0.002077852,0.001282728,0.00417584],"category_scores_gemma":[0.002985446,0.0006551004,0.001380532,0.0008083031,0.001264957,0.001588046,0.002643881,0.002423118,0.001157394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007229177,"about_ca_system_score_gemma":0.0008861109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001447055,"about_ca_topic_score_gemma":0.002085556,"domain_scores_codex":[0.9992669,0.0002046693,0.00003260036,0.0002166944,0.0001957524,0.00008330742],"domain_scores_gemma":[0.9988921,0.0005757734,0.00007047876,0.0002678752,0.0001402159,0.00005352805],"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":[0.0001793314,0.0001536057,0.001391896,0.0001568682,0.00009644881,0.0001882932,0.0001367812,0.7553155,0.01006449,0.02166455,0.007291507,0.2033606],"study_design_scores_gemma":[0.00000857952,0.00002405087,0.0000592637,0.000008105229,0.000006825599,0.00003797332,0.000009632811,0.9894742,0.002125874,0.006737526,0.001500869,0.000007013856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00972426,0.0003224706,0.9856986,0.0001988949,0.00006779836,0.0000551972,0.000172674,0.00172415,0.002035773],"genre_scores_gemma":[0.5071397,0.0003446678,0.4816457,0.0008184474,0.0001326363,0.0004722605,0.001621165,0.0008427199,0.006982639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00417584,"threshold_uncertainty_score":0.0139696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1968259845324093,"score_gpt":0.3398827807319934,"score_spread":0.1430567961995841,"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."}}