{"id":"W4387364736","doi":"10.59934/jaiea.v3i1.268","title":"Grouping Patient Data Based On Work And Place Of Residence On Perceived Complaints","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; Residence; Work (physics); Anticipation (artificial intelligence); Cluster (spacecraft); Medicine; Data mining; Computer science; Artificial intelligence; Engineering","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":[0.0009325672,0.0003809631,0.0005378264,0.002597874,0.0005096119,0.0009470856,0.0003972163,0.0004294023,0.00249238],"category_scores_gemma":[0.0050602,0.0001072167,0.0006549509,0.003807179,0.0002776659,0.0004223647,0.0005624442,0.000451726,0.0008190555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004761728,"about_ca_system_score_gemma":0.0007367929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004444677,"about_ca_topic_score_gemma":0.007032493,"domain_scores_codex":[0.9985573,0.0002972862,0.0002953755,0.0002470594,0.0004761633,0.0001268575],"domain_scores_gemma":[0.9955353,0.001784333,0.000757599,0.0004345408,0.001251347,0.0002368613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001167691,0.0006306236,0.8688787,0.0005120017,0.0002236679,0.0005271229,0.002112845,0.005924183,0.006742181,0.0006601018,0.004228652,0.1083922],"study_design_scores_gemma":[0.00006342955,0.000898916,0.9459931,0.0001555505,0.000171426,0.0009440701,0.008981603,0.02435214,0.00644103,0.001126162,0.01077362,0.00009890897],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718603,0.0002370241,0.009843585,0.0006235683,0.00008571745,0.0004894517,0.01326993,0.0001759425,0.003414314],"genre_scores_gemma":[0.9587202,0.0002508537,0.02080048,0.0001007047,0.00003656605,0.0003949753,0.01763274,0.00001876552,0.002044775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004444677,"threshold_uncertainty_score":0.00883764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05806448924047389,"score_gpt":0.2981837313356661,"score_spread":0.2401192420951922,"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."}}