{"id":"W6886860341","doi":"10.15456/jae.2022321.0714439773","title":"A MOMENT-MATCHING METHOD FOR APPROXIMATING VECTOR AUTOREGRESSIVE PROCESSES BY FINITE-STATE MARKOV CHAINS (replication data)","year":2014,"lang":"en","type":"other","venue":"ZBW Journal Data Archive","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain; Autoregressive model; Range (aeronautics); Variable-order Markov model; Markov process; Multivariate statistics; Markov model; Markov kernel","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.005026697,0.001057276,0.001330321,0.0009619203,0.0006497037,0.0007523246,0.007373804,0.000237524,0.0003334396],"category_scores_gemma":[0.007979822,0.0009257253,0.0001428311,0.0003813326,0.0001832057,0.001150082,0.002868397,0.001546629,0.0003752332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888755,"about_ca_system_score_gemma":0.0007896564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002104033,"about_ca_topic_score_gemma":0.000471764,"domain_scores_codex":[0.9926783,0.001094498,0.001402859,0.00248677,0.001150265,0.001187341],"domain_scores_gemma":[0.9835453,0.003153577,0.005670913,0.006844081,0.0002419741,0.0005441318],"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":[0.0002012833,0.0001864199,0.000008242308,0.001729662,0.0009482328,0.00002772691,0.001237836,0.00009996032,0.001833815,0.00009437073,0.9540845,0.03954795],"study_design_scores_gemma":[0.001394647,0.0001148374,0.000006331799,0.00313329,0.0004854547,0.0002741205,0.0001959962,0.1152703,0.0001085191,0.002145013,0.8758581,0.001013388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00000265709,0.001391337,0.7075521,0.0002414248,0.0002682313,0.001664348,0.2810793,0.000318961,0.007481681],"genre_scores_gemma":[0.0000167059,0.000749338,0.7079639,0.0002247025,0.002266515,0.00030867,0.2410715,0.002810664,0.04458806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1151704,"threshold_uncertainty_score":0.9993193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05324130020991069,"score_gpt":0.357111649381935,"score_spread":0.3038703491720243,"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."}}