{"id":"W4205150246","doi":"10.46880/jmika.vol5no2.pp142-146","title":"RANCANGAN PENELITIAN MODEL HYBRID DETEKSI COVID-19 MENGGUNAKAN MARINE PREDATORS ALGORITHM (MPA) DAN INTERPOLASI LINIER","year":2021,"lang":"en","type":"article","venue":"METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Outbreak; Coronavirus disease 2019 (COVID-19); Government (linguistics); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Interpolation (computer graphics); Computer science; Algorithm; Process (computing); Test (biology); Geography; Simulation; Operations research; Mathematics; Artificial intelligence; Medicine; Virology; Ecology; Biology; Pathology","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002098737,0.0008218333,0.0009330253,0.0005854536,0.0009297996,0.001570082,0.003093566,0.0001973935,0.00005481136],"category_scores_gemma":[0.0004880173,0.00084202,0.0003950735,0.001157979,0.000186257,0.002859261,0.003041963,0.001152167,0.00007760747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004477162,"about_ca_system_score_gemma":0.00112917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001407914,"about_ca_topic_score_gemma":0.0001140552,"domain_scores_codex":[0.9942443,0.0006175395,0.001721165,0.001208195,0.001009737,0.001199068],"domain_scores_gemma":[0.9945618,0.0004203953,0.0008010142,0.002545917,0.0003845368,0.001286368],"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":[0.00004853568,0.0002823939,0.001051891,0.0002777934,0.0004446471,0.0003634064,0.008025285,0.01084221,0.0002763968,0.02781181,0.009455347,0.9411203],"study_design_scores_gemma":[0.001588042,0.0001807153,0.0009559148,0.00007643585,0.00008894732,0.00110697,0.0006423516,0.7605949,0.001466022,0.002174833,0.2301697,0.0009552003],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0163429,0.0001045728,0.9722502,0.002612439,0.0007931162,0.0004997351,0.0001355634,0.0008146221,0.006446903],"genre_scores_gemma":[0.05193917,0.0001815472,0.9357033,0.009146418,0.0003972361,0.0001882488,0.001017973,0.0001049328,0.001321134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9401651,"threshold_uncertainty_score":0.9994664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188164560062636,"score_gpt":0.2902868972471394,"score_spread":0.2714704412408758,"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."}}