{"id":"W6942853535","doi":"10.15456/jae.2022326.0707786066","title":"Efficient estimation of Bayesian VARMAs with time‐varying coefficients (replication data)","year":2017,"lang":"en","type":"other","venue":"ZBW Journal Data Archive","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Estimation; Bayesian probability; Gibbs sampling; Class (philosophy); Autoregressive model; Bayes estimator; Work (physics); Bayesian vector autoregression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01585301,0.0006789853,0.001675106,0.001352645,0.0006455013,0.001975053,0.002636018,0.001796172,0.00410718],"category_scores_gemma":[0.08064587,0.001161929,0.001458875,0.001791447,0.0008931337,0.002451221,0.001885075,0.002491887,0.001255889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008406382,"about_ca_system_score_gemma":0.001852372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01481643,"about_ca_topic_score_gemma":0.01651924,"domain_scores_codex":[0.9957546,0.00302801,0.0001554677,0.0006079792,0.0003182117,0.0001356367],"domain_scores_gemma":[0.9644349,0.02743281,0.002005317,0.004184452,0.001643315,0.0002993302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003384997,0.0001563016,0.0296448,0.0003291502,0.0006402938,0.0002807138,0.0004912155,0.5594608,0.00169495,0.1761848,0.007783529,0.222995],"study_design_scores_gemma":[0.00004811415,0.00003482369,0.002841231,0.00005350008,0.00004551153,0.00006361943,0.00005273321,0.912813,0.0003996615,0.08054165,0.003071879,0.00003431782],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.06719711,0.0008557484,0.9274886,0.0006481375,0.00008843727,0.0001270881,0.0007712835,0.0007236957,0.002099836],"genre_scores_gemma":[0.6092299,0.0008036317,0.3821864,0.0002277629,0.000215591,0.000364301,0.00296361,0.000272563,0.003736078],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01585301,"threshold_uncertainty_score":0.08383977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03058992835150845,"score_gpt":0.2725650461434924,"score_spread":0.241975117791984,"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."}}