{"id":"W6926074598","doi":"10.21227/dsyh-8249","title":"Locally Linear Embedding and fMRI feature selection in psychiatric classification","year":2019,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009756762,0.001870367,0.0009863925,0.002097934,0.0007380613,0.000973348,0.002376839,0.001770446,0.01205385],"category_scores_gemma":[0.00357718,0.0003487484,0.001357624,0.00234692,0.0004516969,0.0005855224,0.001520704,0.001493333,0.0153588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109172,"about_ca_system_score_gemma":0.00108572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327705,"about_ca_topic_score_gemma":0.02596866,"domain_scores_codex":[0.999209,0.0001858391,0.00008062699,0.0002177803,0.0001975147,0.0001092498],"domain_scores_gemma":[0.9989669,0.0003126921,0.00009455916,0.0003224683,0.0002162993,0.00008714222],"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":[0.0008707067,0.000399477,0.007266523,0.00132321,0.000236357,0.0003541699,0.00006356248,0.004044631,0.001671664,0.0007530906,0.9341593,0.0488572],"study_design_scores_gemma":[0.001777531,0.0008738772,0.1023141,0.001144376,0.0005220917,0.002688445,0.0006763234,0.03141303,0.01287529,0.007793604,0.837586,0.0003353319],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0229974,0.001343699,0.003188317,0.0006783109,0.0002982251,0.000233017,0.9655262,0.002109907,0.003625071],"genre_scores_gemma":[0.01215603,0.0002415399,0.002840171,0.0001263307,0.00003526244,0.0003345952,0.9824902,0.00007916725,0.00169661],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01327705,"threshold_uncertainty_score":0.04032415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520764065390933,"score_gpt":0.3075745956749424,"score_spread":0.2823669550210331,"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."}}