{"id":"W3123483426","doi":"","title":"Common large innovations across nonlinear time series","year":2002,"lang":"en","type":"preprint","venue":"Data Archiving and Networked Services (DANS)","topic":"Italy: Economic History and Contemporary Issues","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Nonlinear system; Unemployment; Multivariate statistics; Inference; Series (stratigraphy); Autoregressive model; Latent variable; Econometric model; Time series; Representation (politics); Nonlinear autoregressive exogenous model; Economics; Mathematics; Computer science; Statistics; Artificial intelligence; Macroeconomics","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":[0.001086737,0.0004942098,0.0009968361,0.0001785069,0.0006599257,0.0003880298,0.00194997,0.0002814671,0.0002758133],"category_scores_gemma":[0.00001398305,0.0005979227,0.0001067668,0.0002014318,0.0001573111,0.0008451353,0.00325527,0.0007586489,0.0006261228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006016331,"about_ca_system_score_gemma":0.0000239919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007808071,"about_ca_topic_score_gemma":0.001394598,"domain_scores_codex":[0.9967833,0.00006687843,0.001140582,0.001336726,0.0000505537,0.0006220138],"domain_scores_gemma":[0.996692,0.00009745412,0.0008002594,0.002247179,0.00002500871,0.0001380889],"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":[0.000651486,0.002197926,0.6617711,0.008098226,0.003872078,0.00028631,0.1716138,0.006751359,0.0000680466,0.06648669,0.05578889,0.0224141],"study_design_scores_gemma":[0.0005071871,0.00005196096,0.01142057,0.000374928,0.00002880971,0.00001492843,0.0007578967,0.2886669,0.000003410179,0.008645466,0.6884727,0.001055145],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9012778,0.009800599,0.0009915163,0.0006219367,0.0013729,0.0005293097,0.04238334,0.0003000908,0.04272249],"genre_scores_gemma":[0.9481101,0.00220991,0.002583539,0.0007993911,0.001425189,0.00006543194,0.03012458,0.000148959,0.01453287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6503505,"threshold_uncertainty_score":0.9996472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03850246792434551,"score_gpt":0.2464587375601315,"score_spread":0.207956269635786,"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."}}