{"id":"W4387377085","doi":"10.59934/jaiea.v3i1.275","title":"Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Cluster analysis; Slum; Environmental health; Cluster (spacecraft); Data mining; Human settlement; Computer science; Medicine; Geography; Machine learning; Population","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":[0.0005114984,0.0006052554,0.000599505,0.004522376,0.0008364023,0.001010738,0.0007284679,0.0005436139,0.002517126],"category_scores_gemma":[0.002127037,0.0001682284,0.0009791421,0.003485797,0.0003311195,0.0004554464,0.0008070224,0.0003076603,0.0006801309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007799991,"about_ca_system_score_gemma":0.0009799261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02873498,"about_ca_topic_score_gemma":0.02986554,"domain_scores_codex":[0.9993586,0.0001009169,0.00007598224,0.0001886272,0.0001938203,0.0000819964],"domain_scores_gemma":[0.9990985,0.0002051114,0.0001339956,0.0001174282,0.0003875484,0.00005734768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009076964,0.0005593689,0.5018588,0.0009219079,0.0007368138,0.001122441,0.003524215,0.09102824,0.01858729,0.003316633,0.01313955,0.3642971],"study_design_scores_gemma":[0.00007116207,0.0004950516,0.4670297,0.0002141535,0.0003819057,0.001443161,0.006503301,0.4828463,0.01820584,0.004301059,0.01829841,0.0002099293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8886648,0.0005585177,0.09375325,0.0004164756,0.0001437293,0.0007550963,0.007447673,0.001177541,0.007082836],"genre_scores_gemma":[0.9277615,0.0002652114,0.06175236,0.00003217076,0.00002170611,0.0003467677,0.006272156,0.00004627501,0.003501912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02873498,"threshold_uncertainty_score":0.05713546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03741071132049064,"score_gpt":0.3105275491778416,"score_spread":0.273116837857351,"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."}}