{"id":"W2898179508","doi":"10.35799/dc.6.2.2017.17837","title":"Prediksi Tinggi Gelombang Laut di Perairan Laut Sulawesi Utara dengan Menggunakan Model Vector Autoregressive (VAR)","year":2017,"lang":"id","type":"article","venue":"d CARTESIAN","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Physics; Forestry; Geography","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":[0.00134515,0.0009664959,0.0008082237,0.0008640309,0.0004774666,0.003494879,0.0008091442,0.0007237577,0.009975453],"category_scores_gemma":[0.003302739,0.0005956903,0.001294329,0.001261538,0.0005072994,0.002071716,0.001026839,0.001722698,0.003663335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522492,"about_ca_system_score_gemma":0.002026981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02119275,"about_ca_topic_score_gemma":0.02654732,"domain_scores_codex":[0.9992332,0.0001541447,0.00005320463,0.0002434751,0.0002240981,0.00009182552],"domain_scores_gemma":[0.9987464,0.0004249783,0.000200025,0.0001605212,0.0004075725,0.00006053492],"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":[0.0008429568,0.0005947611,0.1478496,0.001400399,0.001149829,0.001044868,0.002370179,0.1580684,0.02565701,0.06507599,0.03701612,0.5589298],"study_design_scores_gemma":[0.0001469904,0.0009478004,0.233569,0.0005988039,0.00138641,0.000840342,0.003195471,0.4821109,0.03012274,0.04940558,0.1971781,0.0004979712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4376999,0.00545669,0.4369965,0.006091157,0.0009710036,0.0004871887,0.01210756,0.005064771,0.09512505],"genre_scores_gemma":[0.8813279,0.003760403,0.0388642,0.0004768358,0.0001523921,0.0002438884,0.006410093,0.0004030738,0.06836116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02119275,"threshold_uncertainty_score":0.04213881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333306138832343,"score_gpt":0.2894422023763403,"score_spread":0.2661091409880169,"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."}}