{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008671393,0.00007373888,0.0002262496,0.0001040989,0.00003856838,0.00002188412,0.0002746305,0.00004822841,0.0005175408],"category_scores_gemma":[0.004738035,0.00006580618,0.00002499432,0.00007009408,0.0000728996,0.00007421753,0.000009928644,0.0001096412,0.000006308904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005291999,"about_ca_system_score_gemma":0.001249556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007644547,"about_ca_topic_score_gemma":0.0002032326,"domain_scores_codex":[0.9990504,0.00006565565,0.0004687931,0.0000978662,0.0001604227,0.0001569073],"domain_scores_gemma":[0.9959142,0.002618859,0.0003767085,0.0002573753,0.000603313,0.0002296016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000672644,0.00002458909,0.0007920937,0.0001421786,0.0000656561,0.000007551358,0.0001017572,0.0002222347,0.0006440838,0.8979587,0.0874628,0.01251113],"study_design_scores_gemma":[0.001284321,0.0009849654,0.017473,0.0001796107,0.0002564725,0.0000790157,0.0003904941,0.1484657,0.0002898683,0.8197603,0.01055217,0.000284069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002584823,0.00003465843,0.994105,0.00008432553,0.0004114074,0.0001575117,0.002381412,0.000001520978,0.0002392835],"genre_scores_gemma":[0.09990158,0.000003788749,0.8998169,0.00002475575,0.00007796499,0.000001271563,0.00003333138,0.00001237607,0.0001280764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1482435,"threshold_uncertainty_score":0.5672212,"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."}}