{"id":"W2961984786","doi":"10.1111/rssa.12491","title":"UK Regional Nowcasting Using a Mixed Frequency Vector Auto-Regressive Model with Entropic Tilting","year":2019,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Nowcasting; Autoregressive model; Aggregate (composite); Econometrics; Computer science; Exploit; Vector autoregression; Economics; Geography; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001836178,0.000477725,0.0006089285,0.0005521775,0.0001896178,0.001282778,0.0009080258,0.0009043352,0.002240449],"category_scores_gemma":[0.007864974,0.0004882351,0.0007984948,0.0008051501,0.0003975992,0.001253174,0.0007015429,0.001322842,0.000396744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008543151,"about_ca_system_score_gemma":0.0008951657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05175754,"about_ca_topic_score_gemma":0.03714771,"domain_scores_codex":[0.9994414,0.000222982,0.00004104665,0.0001471748,0.00009574417,0.00005151605],"domain_scores_gemma":[0.9981381,0.0009185531,0.0003299875,0.0002403488,0.0003054398,0.00006759705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006137028,0.00001148172,0.002609032,0.00003144251,0.00004963196,0.00005985575,0.00005829248,0.9683073,0.0008661644,0.01181648,0.001308036,0.01482091],"study_design_scores_gemma":[0.00000283312,0.000005349711,0.000525454,0.000003337314,0.000005800674,0.000003717838,0.0000057129,0.997277,0.0001334919,0.001640452,0.0003897865,0.000007072988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.235489,0.000530055,0.7543297,0.00140067,0.0003790301,0.00005465306,0.002007306,0.001105615,0.004703977],"genre_scores_gemma":[0.9448943,0.000295843,0.0486536,0.0001229213,0.0001317166,0.00004347489,0.001354882,0.0001339269,0.004369327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05175754,"threshold_uncertainty_score":0.1029125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05528175492662648,"score_gpt":0.3289328123172136,"score_spread":0.2736510573905871,"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."}}