{"id":"W4394426635","doi":"10.6084/m9.figshare.11350370","title":"MoCA Test: normative and diagnostic accuracy data for seniors with heterogeneous educational levels in Brazil","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Test (biology); Gerontology; Psychology; Computer science; Medicine; Political science; Biology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002487024,0.0004610204,0.0004538359,0.004291544,0.0004235087,0.0007489427,0.00062751,0.0004283414,0.001089419],"category_scores_gemma":[0.01167087,0.0001975995,0.0005827206,0.001789782,0.0004892274,0.0003526391,0.0006212211,0.0002584008,0.0003294956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007379533,"about_ca_system_score_gemma":0.0007581714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0382858,"about_ca_topic_score_gemma":0.03369513,"domain_scores_codex":[0.9989886,0.0002171048,0.0002062724,0.0001716769,0.0003295522,0.00008690534],"domain_scores_gemma":[0.9961504,0.0008642673,0.001180733,0.0003315672,0.001239321,0.0002337664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001677886,0.0000738669,0.9786336,0.0001304909,0.0001364349,0.000112221,0.0004040046,0.0001574648,0.000501596,0.0001489963,0.00102409,0.01850942],"study_design_scores_gemma":[0.00001726502,0.00009959922,0.9970702,0.00007262234,0.00006226671,0.0006713865,0.0002145469,0.0004841908,0.0002160194,0.0001310979,0.0009509142,0.000009894611],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9886671,0.001845362,0.001084682,0.0001165341,0.00003702399,0.0001174257,0.003978507,0.00006359403,0.004089788],"genre_scores_gemma":[0.9963999,0.000326102,0.001071086,0.00003039091,0.000009879627,0.00007245154,0.00189141,0.00001063646,0.0001881456],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.0382858,"threshold_uncertainty_score":0.07612586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2992851840427941,"score_gpt":0.4620902205888051,"score_spread":0.1628050365460109,"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."}}