{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005133605,0.0006414213,0.001296193,0.001745142,0.0006322676,0.001041923,0.002388561,0.001704039,0.004796494],"category_scores_gemma":[0.01989676,0.0008844626,0.001516458,0.001983164,0.0007932036,0.002239063,0.001353615,0.002197443,0.001853191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068829,"about_ca_system_score_gemma":0.001982858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007285987,"about_ca_topic_score_gemma":0.004810177,"domain_scores_codex":[0.9980779,0.00091946,0.0001160812,0.0003691013,0.0003996386,0.0001177394],"domain_scores_gemma":[0.9944523,0.003324678,0.0005413581,0.0009244113,0.0006247732,0.000132479],"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":[0.0003551543,0.0001703123,0.004139594,0.0002036284,0.0002513049,0.0002108376,0.0002021228,0.5951197,0.006895126,0.1121557,0.004088881,0.2762077],"study_design_scores_gemma":[0.00001493735,0.00002086865,0.0003863242,0.00001325453,0.00001266353,0.00004490993,0.000009077026,0.97729,0.001228385,0.01926976,0.001688775,0.00002110059],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.002765283,0.00006655974,0.9965338,0.00003520887,0.00001809399,0.00002072328,0.00007082362,0.0003004447,0.0001890987],"genre_scores_gemma":[0.1613354,0.0002579436,0.8338898,0.00009579777,0.00009484129,0.0002518808,0.001084055,0.0003334733,0.002656775],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.007285987,"threshold_uncertainty_score":0.02714938,"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."}}