{"id":"W4398862989","doi":"10.7910/dvn/zbrtjh/ausinj","title":"Table2Sim3k.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Replication (statistics); Inference; Root (linguistics); Unit root; Computer science; Econometrics; Mathematics; Statistics; Artificial intelligence; Philosophy; Linguistics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003134066,0.0003663833,0.0003944697,0.0001778666,0.00009648538,0.0004493774,0.003724262,0.0003434883,0.004852976],"category_scores_gemma":[0.00008378543,0.0003522248,0.0001119001,0.0002749637,0.00004676468,0.0007284782,0.00148348,0.0005961956,0.42643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005921321,"about_ca_system_score_gemma":0.0003261311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001559045,"about_ca_topic_score_gemma":0.00001677028,"domain_scores_codex":[0.997757,0.00008133971,0.0003212798,0.000875777,0.000483508,0.0004811412],"domain_scores_gemma":[0.9957624,0.00007405515,0.0001900716,0.003699437,0.00009693152,0.0001770874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002966966,0.00003836802,5.378125e-7,0.00006794299,0.00002487698,0.00005382826,0.000007839049,0.00004061572,0.000003893148,0.0009534064,0.9980592,0.000746526],"study_design_scores_gemma":[0.0001750975,0.00004335967,0.000001888807,0.00008555481,0.00003023632,0.00002275116,0.000002904301,0.005269684,0.00001249083,0.0002825897,0.993637,0.0004364116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[4.084064e-7,0.000002174954,0.1101849,0.00001221437,0.00155773,0.0001457483,0.8875986,0.0001135865,0.0003845662],"genre_scores_gemma":[0.000006426512,0.0002354041,0.005053427,0.001048515,0.0001801391,0.00001369218,0.9925201,0.00001376615,0.0009285219],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4215771,"threshold_uncertainty_score":0.999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03057224920142849,"score_gpt":0.2574310480528469,"score_spread":0.2268587988514184,"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."}}