{"id":"W4398589202","doi":"10.7910/dvn/dgykyj","title":"Replication Data for: Endogenous Time Variation in Vector Autoregressions","year":2021,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Replication (statistics); Endogeny; Variation (astronomy); Computer science; Econometrics; Biology; Economics; Virology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001315275,0.0002754499,0.000531286,0.0001258064,0.00009997026,0.00009433111,0.00122736,0.0003232615,0.01070269],"category_scores_gemma":[0.02986948,0.0002596606,0.00005613872,0.0002065116,0.00004397477,0.0001827921,0.0008070676,0.000337807,0.004938018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257692,"about_ca_system_score_gemma":0.0003188973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001830042,"about_ca_topic_score_gemma":0.00008006827,"domain_scores_codex":[0.9974505,0.0002994071,0.000608214,0.001024964,0.0003086831,0.0003082208],"domain_scores_gemma":[0.9899596,0.002597328,0.0003760266,0.006823686,0.0001375278,0.000105812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002371333,0.0001888248,7.858067e-7,0.0002666268,0.00003993819,0.00002999537,0.0000195852,3.200245e-7,0.0003021703,0.003205434,0.9949092,0.001013352],"study_design_scores_gemma":[0.0003661769,0.00003524866,0.00005290846,0.0002660235,0.000205105,0.0000150395,0.000007604964,0.001913455,0.00002295906,0.00884246,0.9879935,0.0002795247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003066328,0.000002232217,0.02312266,0.00001289867,0.0003434005,0.0006430984,0.9757309,0.00004248462,0.0000992839],"genre_scores_gemma":[9.826067e-7,0.00007689442,0.1751569,0.00007108556,0.0002002749,0.0001133009,0.8242482,0.00002521539,0.0001071816],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1520342,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1764763623918957,"score_gpt":0.3819051789599777,"score_spread":0.205428816568082,"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."}}