{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007014113,0.0001571519,0.0001868048,0.0005193781,0.0001223396,0.0002345455,0.000418768,0.00003307521,0.000002066498],"category_scores_gemma":[0.0001066953,0.0001419852,0.00004059563,0.001050168,0.00002357189,0.0004271073,0.0001515103,0.0003185857,0.00002899505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000608765,"about_ca_system_score_gemma":0.00005583912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002803453,"about_ca_topic_score_gemma":0.00002172063,"domain_scores_codex":[0.9987162,0.00003488252,0.0004619136,0.0002848217,0.0002433529,0.0002588161],"domain_scores_gemma":[0.9988857,0.0003193551,0.0001638093,0.0003819337,0.00007669023,0.0001725385],"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":[0.00003341422,0.00007556027,0.0003902362,0.00003678045,0.00002232049,0.00002829563,0.0005612458,0.9000152,0.001018193,0.006764656,0.00008069169,0.09097337],"study_design_scores_gemma":[0.0000635781,0.0001113146,0.004227458,0.0001593546,0.00001455878,0.0000413582,0.0001588851,0.9904795,0.000402021,0.0004296498,0.00372359,0.0001887409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03079317,0.00002922998,0.9676283,0.001149527,0.00007477328,0.0001831855,0.00001015506,0.0001108568,0.00002081833],"genre_scores_gemma":[0.8119408,0.0001008975,0.1875529,0.00009391818,0.0001137456,0.0001024058,0.00001135037,0.00002742546,0.00005648698],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7811477,"threshold_uncertainty_score":0.578999,"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."}}