{"id":"W4244565075","doi":"10.1109/msp.2020.3019612","title":"Table of Contents","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Estadual Paulista; Linköpings Universitet; Menzies Centre for Australian Studies, King's College London, University of London; École Polytechnique Fédérale de Lausanne; York University; Chinese University of Hong Kong; Institut national de recherche en informatique et en automatique (INRIA); University of Pittsburgh; University of Pennsylvania; Purdue University; North Carolina State University; Qualcomm; Kingston University; Arizona State University; Texas Instruments; Iowa State University; Microsoft; Korea Advanced Institute of Science and Technology; National Science Foundation","keywords":"Computer science; Table (database); Information retrieval; Database","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001738112,0.0001041746,0.0003534451,0.00006176647,0.00007169088,0.00004852811,0.0001904564,0.00003598248,0.003146813],"category_scores_gemma":[0.00003473149,0.0001150647,0.00006172805,0.0002352602,0.0001080623,0.000237898,0.00004308149,0.00006852985,0.006498368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001400096,"about_ca_system_score_gemma":0.00002029724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001447453,"about_ca_topic_score_gemma":8.890554e-7,"domain_scores_codex":[0.9990002,0.000003385774,0.0004388139,0.0003233926,0.00003263939,0.0002015995],"domain_scores_gemma":[0.9994693,0.00001191228,0.0002985533,0.0000873814,0.00005106463,0.00008183345],"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":[0.0002288079,0.0003330022,0.02454255,0.0008428133,0.0002838341,0.00002018603,0.004569909,0.00161767,0.01267381,0.02323753,0.916167,0.01548288],"study_design_scores_gemma":[0.002618883,0.000263762,0.002851114,0.0001058719,0.00003281033,0.000003888988,0.0005938211,0.04606064,0.006591612,0.005908635,0.9341657,0.00080322],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03518878,0.004858648,0.01597866,0.001598723,0.001219452,0.0002671757,0.0006159969,0.0001219464,0.9401506],"genre_scores_gemma":[0.9688603,0.00002668365,0.0004362935,0.0005066816,0.0001117344,0.000003274692,0.000003704374,0.00001280812,0.03003851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9336715,"threshold_uncertainty_score":0.9977645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0712105553970094,"score_gpt":0.2213876844573321,"score_spread":0.1501771290603227,"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."}}