{"id":"W6944764816","doi":"10.21227/3kf8-9p07","title":"Locally Linear Embedding and fMRI feature selection in psychiatric classification","year":2019,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Preprocessor; Pattern recognition (psychology); Embedding; Feature selection; Feature (linguistics); Selection (genetic algorithm); Feature extraction; tar (computing)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009762898,0.0006465386,0.0006910859,0.001282535,0.0001198304,0.000150235,0.0006937083,0.001134023,0.0001079314],"category_scores_gemma":[0.0001341645,0.0006799322,0.00008178174,0.001549435,0.00007827208,0.0006107892,0.0001184879,0.001876154,0.005571328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000383176,"about_ca_system_score_gemma":0.0004759004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004046602,"about_ca_topic_score_gemma":0.001485588,"domain_scores_codex":[0.9964068,0.0001652337,0.0007057179,0.001418899,0.0007216082,0.0005817523],"domain_scores_gemma":[0.9975488,0.00006918225,0.0007841057,0.001282832,0.0001515452,0.0001635961],"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":[0.0001225116,0.0001512455,0.001390146,0.0003247632,0.00004973264,0.0000220555,0.00000950286,0.0002210718,0.0004629638,0.000002071929,0.9970663,0.0001775876],"study_design_scores_gemma":[0.0007427952,0.0000893632,0.006183967,0.0001626509,0.0002100961,0.0001387698,0.00002260712,0.005606421,0.00002024752,0.000007657849,0.9861175,0.0006979493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007997334,0.0002325945,0.000161766,0.00007803791,0.001295082,0.0009765618,0.9962828,0.0001127245,0.000060704],"genre_scores_gemma":[0.0002203922,0.000409669,0.001079917,0.0001366184,0.001044688,0.00006724548,0.9965675,0.0001441999,0.0003298051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01094887,"threshold_uncertainty_score":0.9995652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03139472175222167,"score_gpt":0.3286543342013148,"score_spread":0.2972596124490931,"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."}}