{"id":"W4400649357","doi":"10.26102/2310-6018/2018.23.4.012","title":"ПРОГНОЗИРОВАНИЕ НЕСТАЦИОНАРНЫХ ВРЕМЕННЫХ РЯДОВ НА ОСНОВЕ МУЛЬТИВЕЙВЛЕТНОЙ ПОЛИМОРФНОЙ СЕТИ","year":2018,"lang":"en","type":"article","venue":"Modelirovanie, optimizaciâ i informacionnye tehnologii.","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0099941,0.001408149,0.001755717,0.002246718,0.001700302,0.001368133,0.005766119,0.001269816,0.005307392],"category_scores_gemma":[0.01790949,0.001128173,0.0008376208,0.004227989,0.001889767,0.002992261,0.002470396,0.001680601,0.007267047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004259851,"about_ca_system_score_gemma":0.0005464861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008207811,"about_ca_topic_score_gemma":0.00007045428,"domain_scores_codex":[0.9866313,0.0007351947,0.003718012,0.002279369,0.003915074,0.002721041],"domain_scores_gemma":[0.9876423,0.002577376,0.001503153,0.004592921,0.002775261,0.0009089275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001591195,0.0005464843,0.0054154,0.00005915868,0.0003596326,0.0001321545,0.003972728,0.05147085,0.0008478435,0.0261741,0.1429977,0.7664327],"study_design_scores_gemma":[0.003400463,0.001476359,0.00134728,0.00009914213,0.0001365298,0.000554311,0.002056365,0.7350004,0.003819164,0.06326887,0.1862877,0.00255351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1596716,0.000361256,0.5633714,0.003213399,0.005078015,0.001642321,0.000152296,0.002428435,0.2640812],"genre_scores_gemma":[0.6926805,0.0001160442,0.2867917,0.003219893,0.001000616,0.000204592,0.00005130022,0.0001798017,0.01575551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7638792,"threshold_uncertainty_score":0.9998669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1586984320354769,"score_gpt":0.4166397304827893,"score_spread":0.2579412984473124,"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."}}