{"id":"W6898657304","doi":"10.57760/sciencedb.10751","title":"Raw Data of MoCA Normative Data in Mainland China","year":2024,"lang":"en","type":"dataset","venue":"ScienceDB","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Raw data; Mainland China; China; Mainland; Data collection","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":[0.001615737,0.001341438,0.001219263,0.003434662,0.0007198714,0.0008552945,0.002066954,0.001008667,0.01620223],"category_scores_gemma":[0.006416221,0.0003834928,0.0007361549,0.005292987,0.0004557093,0.0006001482,0.001101211,0.000793026,0.01979522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373208,"about_ca_system_score_gemma":0.002999732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07773446,"about_ca_topic_score_gemma":0.08867088,"domain_scores_codex":[0.99906,0.0001329505,0.0001407866,0.0003175447,0.0002273397,0.0001213832],"domain_scores_gemma":[0.9979129,0.0003020945,0.0001683653,0.0005091032,0.0009385951,0.0001689469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005087358,0.0001670359,0.03004111,0.0008698067,0.0002180484,0.0002249247,0.0001265081,0.0007950522,0.000777424,0.0006643574,0.9444852,0.02112172],"study_design_scores_gemma":[0.0007046322,0.0001701489,0.3023202,0.0007153774,0.0003522659,0.0005740914,0.0003988098,0.002516794,0.001858329,0.002529847,0.6876695,0.0001898999],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006428476,0.0001975821,0.0002473923,0.00006830386,0.000045656,0.00007859199,0.9916787,0.0002617061,0.0009935196],"genre_scores_gemma":[0.003741343,0.00006754294,0.0002477796,0.0000288601,0.00001139877,0.0003344062,0.9947932,0.00002764011,0.0007478683],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07773446,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0756689593571463,"score_gpt":0.3769063775748369,"score_spread":0.3012374182176906,"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."}}