{"id":"W4388200291","doi":"10.1007/978-3-031-41925-6_2","title":"Obesity Level Prediction Using Multinomial Logistic Regression","year":2023,"lang":"en","type":"book-chapter","venue":"˜The œSpringer series in applied machine learning","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multinomial logistic regression; Logistic regression; Obesity; Statistics; Multinomial distribution; Medicine; Econometrics; Mathematics; Internal medicine","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.002023881,0.0006947981,0.0008642491,0.0003917903,0.002595705,0.0000359002,0.0005993159,0.001307568,0.0005561732],"category_scores_gemma":[0.0003571413,0.0005758088,0.0001538798,0.0001883296,0.0003671718,0.0001004632,0.001085604,0.00734831,0.001087721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009840046,"about_ca_system_score_gemma":0.0004131242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004127353,"about_ca_topic_score_gemma":0.01454455,"domain_scores_codex":[0.9953949,0.0004049679,0.001616182,0.0009539427,0.0006326284,0.0009973319],"domain_scores_gemma":[0.9964367,0.001267085,0.001135052,0.0008305616,0.0001649489,0.0001656908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004534022,0.0002104258,0.3176339,0.008485063,0.0004949445,0.0005254417,0.04895712,0.09767319,0.003628012,0.3968606,0.001939203,0.119058],"study_design_scores_gemma":[0.002158,0.0005284489,0.01993502,0.01397821,0.000565974,0.00005193287,0.01271632,0.3864346,0.0002319397,0.09977274,0.4589281,0.004698687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06805475,0.002049764,0.01283823,0.002184859,0.02771399,0.01849758,0.0009869797,0.006317889,0.861356],"genre_scores_gemma":[0.6182598,0.0005945409,0.001632378,0.0003272613,0.003057154,0.0003575676,0.0003033078,0.0005749983,0.374893],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5502051,"threshold_uncertainty_score":0.999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.229122154265184,"score_gpt":0.4126419389628257,"score_spread":0.1835197846976418,"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."}}