{"id":"W2995020774","doi":"10.1002/cjs.11531","title":"Optimal design for classification of functional data","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Computer science; Sampling (signal processing); Linear discriminant analysis; Data mining; Optimal design; Functional design; Functional data analysis; Data classification; Data point; Machine learning; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.02294602,0.00116313,0.001965751,0.001534002,0.0005174342,0.001545194,0.001322932,0.002018327,0.002472851],"category_scores_gemma":[0.05880373,0.00101317,0.000963732,0.0008595748,0.002754463,0.001722213,0.001852147,0.001936526,0.0007081051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00189928,"about_ca_system_score_gemma":0.001710545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008671093,"about_ca_topic_score_gemma":0.0005834228,"domain_scores_codex":[0.981481,0.01381741,0.000568849,0.002134407,0.001447048,0.0005513127],"domain_scores_gemma":[0.9601939,0.02983845,0.002935635,0.002353518,0.003977995,0.0007005449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002265545,0.000472714,0.006908508,0.0005402674,0.0002331003,0.000122112,0.000325483,0.6113209,0.009808455,0.1441319,0.002744364,0.2211268],"study_design_scores_gemma":[0.0002113499,0.0005260009,0.001193239,0.00005401485,0.00003498334,0.00005289731,0.00003670693,0.9221947,0.0023787,0.07186209,0.001415594,0.00003964025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008982412,0.0001071601,0.9902549,0.0001636682,0.0000199677,0.00008017828,0.00002775325,0.0000602436,0.0003037207],"genre_scores_gemma":[0.5049031,0.000228502,0.4918317,0.0002727473,0.000106173,0.0009918076,0.0002662381,0.00008777133,0.00131199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02294602,"threshold_uncertainty_score":0.1213516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4685548347863734,"score_gpt":0.3771912776327229,"score_spread":0.09136355715365052,"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."}}