{"id":"W7128631620","doi":"10.26180/5085358","title":"Coherent Predictions of Low Count Time Series","year":2017,"lang":"","type":"article","venue":"Monash University","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Autoregressive model; Interval (graph theory); Count data; Integer (computer science); Point estimation; Sample (material); Time series; Point (geometry)","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":[0.0003253125,0.0002011991,0.0005612685,0.0001678404,0.001289468,0.00008739252,0.00067497,0.0002339195,0.0006716802],"category_scores_gemma":[0.0001120797,0.0003037159,0.0002512412,0.00009839236,0.00042003,0.0008946251,0.0003749963,0.000230271,0.0003088224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000279926,"about_ca_system_score_gemma":0.0001131886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001663597,"about_ca_topic_score_gemma":0.0002807523,"domain_scores_codex":[0.9986802,0.00001885083,0.0004491534,0.0004703414,0.00006770038,0.0003137566],"domain_scores_gemma":[0.9979226,0.00002849194,0.0008347332,0.0009465878,0.0001441303,0.0001234768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008260141,0.001235965,0.8261116,0.0005144782,0.0004934075,0.00005117932,0.003960023,0.004235254,0.0001622194,0.1532193,0.00539639,0.003794243],"study_design_scores_gemma":[0.00180638,0.0003639491,0.696499,0.0002278085,0.00009740692,0.000002180524,0.0005199484,0.1860305,0.0002766967,0.007086446,0.1063714,0.0007182415],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522466,0.0006548606,0.006960642,0.0005298221,0.0009505753,0.0003130817,0.002457808,0.00002971699,0.03585686],"genre_scores_gemma":[0.9793137,0.001537373,0.0003212327,0.000007584536,0.0000883329,3.933087e-7,0.00001743842,0.00001445699,0.01869949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1817953,"threshold_uncertainty_score":0.9999415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02020701968580017,"score_gpt":0.1885880068080899,"score_spread":0.1683809871222897,"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."}}