{"id":"W2902163128","doi":"","title":"Multidimensional and Longitudinal Indicators in Population Health.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Population; Environmental health; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009639946,0.0003379569,0.0003959507,0.003365397,0.0004696453,0.0009902767,0.0004246678,0.0007785684,0.002473535],"category_scores_gemma":[0.03598263,0.0002494424,0.0006427336,0.00494969,0.0005365116,0.001620708,0.001878312,0.001352492,0.000320455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008538081,"about_ca_system_score_gemma":0.001182135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01151329,"about_ca_topic_score_gemma":0.01670304,"domain_scores_codex":[0.9953054,0.003416794,0.0003844823,0.0002496995,0.0004315201,0.0002121261],"domain_scores_gemma":[0.9785475,0.01031787,0.005088953,0.002240961,0.002717763,0.001087026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008953511,0.00008505218,0.9671044,0.00009754825,0.0003061352,0.00002022108,0.0003289081,0.0006107023,0.00005307851,0.003498506,0.004719229,0.02308683],"study_design_scores_gemma":[0.00001143828,0.0001552028,0.9849835,0.0001712315,0.000111845,0.00008324694,0.0007994843,0.002384773,0.0001290024,0.005237864,0.005899071,0.0000333339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8294687,0.03212129,0.02881807,0.02119395,0.002028736,0.0003050267,0.06821758,0.0003738634,0.01747288],"genre_scores_gemma":[0.9807689,0.002818173,0.006202661,0.0004162112,0.0002395755,0.0004172301,0.007884264,0.00001459159,0.001238552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01151329,"threshold_uncertainty_score":0.05098152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4572000486189208,"score_gpt":0.5408046215233402,"score_spread":0.08360457290441936,"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."}}