{"id":"W3124007508","doi":"","title":"Nowcasting GDP with electronic payments data","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Payment; Quarter (Canadian coin); Debit card; Payment card; Database transaction; Economics; Econometrics; Credit card; Business; Finance; Computer science; Geography; Database","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004255606,0.0005019999,0.0007532116,0.001089411,0.000265728,0.0008233939,0.002318913,0.0003067047,0.0002591101],"category_scores_gemma":[0.0008879701,0.0004841086,0.0001130558,0.0005826376,0.0002042548,0.001076749,0.006557543,0.001823368,0.0001728989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000902,"about_ca_system_score_gemma":0.0008556116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001486341,"about_ca_topic_score_gemma":0.006192848,"domain_scores_codex":[0.9952722,0.00008025179,0.0008151929,0.001702784,0.0007235619,0.001406008],"domain_scores_gemma":[0.9961058,0.0001524583,0.0005593089,0.002608554,0.0005163114,0.00005749718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005820519,0.0004757647,0.8887911,0.0009510529,0.0002627348,0.0001714868,0.0001495043,0.009280534,0.00004945234,0.002153545,0.00436612,0.0927666],"study_design_scores_gemma":[0.003077649,0.0001243668,0.05081391,0.001707281,0.0003161804,0.00001045955,0.0007497885,0.3659263,0.00001940339,0.009107098,0.565512,0.002635575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144436,0.0002208085,0.000004385028,0.000280378,0.0003751444,0.001075131,0.00006458231,0.0001078615,0.08342812],"genre_scores_gemma":[0.991133,0.0009727638,0.0004736907,0.000304109,0.001886861,0.0001255225,0.002129853,0.0001591408,0.00281508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8379772,"threshold_uncertainty_score":0.999761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06406739751356566,"score_gpt":0.3137355365260569,"score_spread":0.2496681390124912,"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."}}