{"id":"W4281481149","doi":"10.3390/math10111792","title":"On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Mean squared error; Autoregressive model; Mean absolute percentage error; Estimator; Statistics; Artificial neural network; Autoregressive–moving-average model; Support vector machine; Computer science; Parametric model; Parametric statistics; Statistical model; Mathematics; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004601886,0.0009663895,0.001000133,0.002514279,0.0004993699,0.001136057,0.001708836,0.001084698,0.000815769],"category_scores_gemma":[0.01096986,0.0003440014,0.001512261,0.002212685,0.0008167326,0.001897367,0.001040152,0.00167982,0.0002136935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009198433,"about_ca_system_score_gemma":0.0009527894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008642854,"about_ca_topic_score_gemma":0.004909534,"domain_scores_codex":[0.99871,0.0005827496,0.00007797894,0.0002711854,0.0002525978,0.0001054219],"domain_scores_gemma":[0.9943115,0.004244481,0.000573971,0.0003511925,0.0004300659,0.00008893351],"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":[0.00005333622,0.00006435466,0.01633766,0.0001234264,0.0001413645,0.0002144695,0.0001393573,0.8850718,0.0007972128,0.03350547,0.001098809,0.06245266],"study_design_scores_gemma":[0.000002049,0.00001670073,0.001564843,0.00001410808,0.000009312331,0.00003821031,0.00001990277,0.9879588,0.0001449217,0.009769748,0.0004495927,0.00001179773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04392512,0.001174738,0.9527723,0.0006218213,0.00005463801,0.00005219297,0.000265405,0.0001944474,0.0009394372],"genre_scores_gemma":[0.8224034,0.002818036,0.1703736,0.0002804087,0.0003012601,0.0002658566,0.001109195,0.00008310998,0.002365079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008642854,"threshold_uncertainty_score":0.02433735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5243380888186432,"score_gpt":0.4327732043872767,"score_spread":0.09156488443136646,"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."}}