{"id":"W2730223542","doi":"10.1093/geroni/igx004.230","title":"AN OBJECTIVE MEASURE OF INDIVIDUAL HEALTH AND AGING FOR POPULATION SURVEYS","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Health and Well-being Studies","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Measure (data warehouse); Computer science; Data mining","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":[0.008688876,0.0007446411,0.0007733345,0.003576876,0.0004788659,0.001527461,0.0009457947,0.0009313871,0.002680483],"category_scores_gemma":[0.02816344,0.0002538636,0.000792205,0.004495645,0.0007581768,0.002038649,0.001698018,0.001173407,0.0009059907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008311995,"about_ca_system_score_gemma":0.0008090402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002258308,"about_ca_topic_score_gemma":0.003940498,"domain_scores_codex":[0.9943922,0.002882237,0.000547827,0.0009257396,0.001118824,0.0001332658],"domain_scores_gemma":[0.9899507,0.003367392,0.00318845,0.001974687,0.001234493,0.0002842257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002029774,0.0003769948,0.6288188,0.001268766,0.001176714,0.0001011673,0.001193811,0.01543732,0.00297736,0.0474272,0.02470596,0.276313],"study_design_scores_gemma":[0.0000546227,0.0009543296,0.7959535,0.0005517695,0.0003178095,0.0005740842,0.001229653,0.05360354,0.003837262,0.06602651,0.07668833,0.0002086254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2745599,0.004011121,0.6347069,0.002236881,0.0006549229,0.001602472,0.05688861,0.0009669114,0.02437224],"genre_scores_gemma":[0.7236668,0.001446586,0.2479358,0.0009230183,0.0003172881,0.002852905,0.01962783,0.0001054916,0.003124285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008688876,"threshold_uncertainty_score":0.04595172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08396291915484817,"score_gpt":0.42669974883924,"score_spread":0.3427368296843918,"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."}}