{"id":"W2964250703","doi":"10.3389/fnagi.2019.00205","title":"Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers","year":2019,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Sahlgrenska Universitetssjukhuset; Riksbankens Jubileumsfond","keywords":"Computer science; Artificial intelligence; Classifier (UML); Machine learning; Interpretability; Speech recognition; Natural language processing","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"],"consensus_categories":[],"category_scores_codex":[0.0004118638,0.0002960976,0.000360479,0.0003198235,0.0002304875,0.0001556444,0.001826757,0.0001195035,0.00002784951],"category_scores_gemma":[0.001542577,0.0002828944,0.00005587919,0.0008046322,0.0004540386,0.0009173209,0.0008163244,0.0006631849,0.00001923395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009885248,"about_ca_system_score_gemma":0.0001520492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001002621,"about_ca_topic_score_gemma":0.00003631264,"domain_scores_codex":[0.995966,0.0003504127,0.0004144764,0.001799575,0.0004712989,0.000998294],"domain_scores_gemma":[0.9979656,0.0002964756,0.0002167869,0.001324124,0.00001525944,0.000181712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002358914,0.00004360241,0.1736516,0.00001035547,0.000001215245,0.0007782904,0.001877028,0.0005650711,0.8213901,0.000003748841,0.0001915742,0.001463885],"study_design_scores_gemma":[0.002069951,0.0001160042,0.01822548,0.0001698437,0.00003067324,0.0002809983,0.00419649,0.7575513,0.2138728,0.0001389413,0.002376235,0.0009712858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910963,0.0002007259,0.001387564,0.0001313232,0.006018249,0.0003806327,0.0002462028,0.0001875296,0.0003514895],"genre_scores_gemma":[0.9917977,0.00004267171,0.005067676,0.002618817,0.0001497219,0.000003221404,0.00002160649,0.00003808527,0.0002604457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7569862,"threshold_uncertainty_score":0.9999623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0544733373474466,"score_gpt":0.3151261585980992,"score_spread":0.2606528212506526,"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."}}