{"id":"W4404783696","doi":"10.18653/v1/2024.emnlp-main.365","title":"Satyrn: A Platform for Analytics Augmented Generation","year":2024,"lang":"en","type":"article","venue":"","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Computer science; Data science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002451841,0.00002892071,0.0000389013,0.00006697984,0.0001678065,0.00005854628,0.0001224022,0.00006639768,0.0004575238],"category_scores_gemma":[0.0001035118,0.00002537688,0.00002930629,0.0001672538,0.00006208396,0.00007602497,0.00001641313,0.00004333658,0.00006159182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005149662,"about_ca_system_score_gemma":0.00007627983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002261623,"about_ca_topic_score_gemma":0.004206834,"domain_scores_codex":[0.9996687,0.00001121924,0.00007899479,0.00007466181,0.00007230404,0.00009414312],"domain_scores_gemma":[0.9997154,0.00009712862,0.00001008096,0.0001143601,0.00003605912,0.00002699254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[9.350964e-7,0.000009827198,0.00002816019,0.000003074604,0.00001192151,1.742738e-7,0.00124214,0.000001501791,0.0004659335,0.8861725,0.02138021,0.09068367],"study_design_scores_gemma":[0.00007062399,0.0000108127,0.00001155825,0.000002866485,0.000007165668,1.49048e-7,0.0007725257,0.1009257,0.0005618536,0.004611889,0.8929829,0.00004193624],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03625542,0.001637323,0.2822229,0.1076759,0.002338324,0.001645214,0.0000231379,0.002937263,0.5652645],"genre_scores_gemma":[0.977791,0.0002134519,0.009466279,0.0002589583,0.0001651245,0.00004190592,0.00002578143,0.000004667904,0.01203285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9415356,"threshold_uncertainty_score":0.5009565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471456273964378,"score_gpt":0.4099894648647944,"score_spread":0.2628438374683566,"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."}}