{"id":"W2947392895","doi":"10.1007/s12571-019-00934-x","title":"Heterogeneous factors predict food insecurity among the elderly in developed countries: insights from a multi-national analysis of 48 countries","year":2019,"lang":"en","type":"article","venue":"Food Security","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Socioeconomic status; Marital status; Food security; Environmental health; Descriptive statistics; Scale (ratio); Food insecurity; Logistic regression; Socioeconomics; Agriculture; Geography; Gerontology; Medicine; Population; Economics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007937679,0.0003884052,0.0009670245,0.0006645086,0.000875608,0.00003463853,0.0006220901,0.0006306886,0.001140305],"category_scores_gemma":[0.0004490056,0.0003132355,0.0002695234,0.001516297,0.0002791383,0.0004216051,0.0002655502,0.001162011,0.0001289512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004650812,"about_ca_system_score_gemma":0.001093974,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006197694,"about_ca_topic_score_gemma":0.1924354,"domain_scores_codex":[0.995043,0.001004257,0.00154485,0.0006268575,0.001091319,0.0006897638],"domain_scores_gemma":[0.9958752,0.001645766,0.0008187065,0.0006437278,0.0008060875,0.0002105389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001999863,0.0002632969,0.8970236,0.0002671793,0.001034622,0.000001791207,0.0884057,0.0003815584,0.000003283631,0.01209523,0.0003182372,0.000005468906],"study_design_scores_gemma":[0.002399658,0.0006834433,0.9630455,0.0002355991,0.0004015519,2.545397e-7,0.008351994,0.008011568,0.00006446175,0.007206828,0.009139253,0.0004599183],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909307,0.000741432,0.00004686633,0.0003152265,0.0008817669,0.001890616,0.004568837,0.0001049062,0.0005196404],"genre_scores_gemma":[0.9981883,0.0001294389,0.00007784418,0.0005053301,0.00009775121,0.0001482321,0.0007906161,0.00002979263,0.0000327096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1862377,"threshold_uncertainty_score":0.999932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0951595744573555,"score_gpt":0.3753049601627966,"score_spread":0.2801453857054411,"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."}}