{"id":"W4318217921","doi":"10.1007/s11357-022-00723-z","title":"Efficient representations of binarized health deficit data: the frailty index and beyond","year":2023,"lang":"en","type":"article","venue":"GeroScience","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Institute of Aging; Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Dimensionality reduction; National Health and Nutrition Examination Survey; Computer science; Fidelity; Binary number; Artificial intelligence; Data mining; Medicine; Mathematics; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004089072,0.00004200636,0.0001110656,0.00004224206,0.0002785136,0.0000107023,0.0001209809,0.00001190705,0.000007421767],"category_scores_gemma":[0.0002086792,0.00002651061,0.00001532803,0.0005954573,0.0002923278,0.00002128101,0.0002521566,0.00005234588,0.000007095226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001081736,"about_ca_system_score_gemma":0.00006926095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001724319,"about_ca_topic_score_gemma":0.00001717126,"domain_scores_codex":[0.9992245,0.00001668016,0.0001326798,0.0002070794,0.000272015,0.0001469969],"domain_scores_gemma":[0.9994111,0.0001329813,0.00005146029,0.0003106402,0.0000398929,0.00005393634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007640581,0.00216059,0.4771984,0.001050751,0.0001478543,0.00006876081,0.008142585,0.002547206,0.06288162,0.06142204,0.3566333,0.02698282],"study_design_scores_gemma":[0.001066986,0.0002175191,0.9733123,0.00005050401,0.00001426909,0.00001704252,0.00267637,0.0152603,0.00008582409,0.0007825732,0.006448032,0.0000682912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721431,0.0006489524,0.0007037275,0.02453218,0.0001686249,0.0003403159,0.0001512564,0.00003724806,0.001274595],"genre_scores_gemma":[0.9983649,0.0002375234,0.0003408085,0.0007256222,0.00002497426,0.000009230392,0.0000231452,0.000002815992,0.0002709494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4961139,"threshold_uncertainty_score":0.2142129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09603091599232198,"score_gpt":0.3788994203815228,"score_spread":0.2828685043892008,"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."}}