{"id":"W4206111896","doi":"10.5267/j.dsl.2021.11.004","title":"Assessing the forecasting model ability in measuring the prevention transmission of COVID-19 pandemic: An application of visibility analysis using Inductive logic","year":2022,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Padjadjaran; Universiti Malaysia Terengganu","keywords":"Visibility; Computer science; Operations research; Transmission (telecommunications); Stakeholder; Pandemic; Econometrics; Coronavirus disease 2019 (COVID-19); Economics; Engineering; Geography; Meteorology; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01353838,0.00009600185,0.0001903313,0.0004169724,0.001021559,0.0001285517,0.002078447,0.00002276054,0.000002088189],"category_scores_gemma":[0.000742861,0.00006334564,0.00009561422,0.005098121,0.0003883109,0.001076887,0.0004954275,0.00031239,1.014392e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980352,"about_ca_system_score_gemma":0.0002924761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005353984,"about_ca_topic_score_gemma":0.00002584403,"domain_scores_codex":[0.997017,0.0005684971,0.0005417907,0.0006676815,0.001003194,0.0002018075],"domain_scores_gemma":[0.9974156,0.0007502087,0.0005031686,0.001157561,0.0001032897,0.00007017174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005798623,0.00006146778,0.02204535,0.000003966287,0.000004434739,1.250732e-7,0.002888975,0.813995,0.05690182,0.000444992,6.903288e-7,0.1036474],"study_design_scores_gemma":[0.0001012649,0.00001821939,0.03024376,0.000005954441,0.00002436586,0.000004671862,0.0007759989,0.9607693,0.0002476673,0.007733107,0.000009356057,0.00006636576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4931417,0.000005129863,0.5061691,0.0004895625,0.00001271202,0.0001616039,0.000001994156,0.00001422158,0.000004019314],"genre_scores_gemma":[0.8856654,5.447611e-7,0.1140293,0.0002568988,0.000004388019,0.00003697596,0.000003325369,0.000002877458,3.22072e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3925238,"threshold_uncertainty_score":0.7857107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1968207004522297,"score_gpt":0.4296458553985255,"score_spread":0.2328251549462958,"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."}}