{"id":"W4391926338","doi":"10.1007/s11227-024-05912-5","title":"Meta generative image and text data augmentation optimization","year":2024,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Discriminator; Computer science; Generator (circuit theory); Generative grammar; Artificial intelligence; Machine learning; Intuition; Domain (mathematical analysis); Generative model; Image (mathematics); Data mining; Pattern recognition (psychology); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001621275,0.00009724987,0.0001619449,0.00007490035,0.0001871045,0.0004574519,0.0006422242,0.00001939896,0.00002013352],"category_scores_gemma":[0.00004878347,0.00005576854,0.0000474052,0.000230791,0.0000470692,0.001883621,0.0003678041,0.0001498357,0.000002834249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001833002,"about_ca_system_score_gemma":0.00004918147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001580808,"about_ca_topic_score_gemma":0.000001800899,"domain_scores_codex":[0.9989856,0.0002688233,0.0002858285,0.0001526197,0.0001924763,0.0001146914],"domain_scores_gemma":[0.9991358,0.0003664262,0.00008661206,0.0002595953,0.000111621,0.00003998064],"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":[0.00001468562,0.00003597293,0.00001836393,0.0000338245,0.001684909,0.00005021362,0.008347887,0.7999647,0.02149356,0.001952667,0.006821813,0.1595814],"study_design_scores_gemma":[0.00008578948,0.00003928211,0.00003104267,0.00002980208,0.0003131103,0.0001406287,0.0002006096,0.9966791,0.001754971,0.0003041523,0.0003566392,0.00006487877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002810393,0.004925701,0.9888989,0.002789932,0.0003991424,0.000057311,0.000002609498,0.00001915944,0.00009683782],"genre_scores_gemma":[0.5022357,0.0003974253,0.4966454,0.0001927157,0.0004906225,2.800676e-7,0.000003119987,0.00000845441,0.00002633175],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4994252,"threshold_uncertainty_score":0.4411219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06707860647216189,"score_gpt":0.2854487057895803,"score_spread":0.2183700993174184,"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."}}