{"id":"W2765401959","doi":"10.1002/cjs.11329","title":"Linear operator‐based statistical analysis: A useful paradigm for big data","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operator (biology); Computer science; Functional data analysis; Kernel principal component analysis; Covariance; Covariance operator; Linear map; Nonparametric statistics; Mathematics; Artificial intelligence; Machine learning; Kernel method; Statistics","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":[0.01674225,0.00141257,0.001485393,0.003181448,0.001049765,0.004760798,0.002140097,0.001519808,0.002648356],"category_scores_gemma":[0.03206036,0.0007007936,0.001930724,0.003790586,0.008961999,0.006004686,0.004398375,0.006530663,0.0007232013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002216469,"about_ca_system_score_gemma":0.003407075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002506013,"about_ca_topic_score_gemma":0.001753074,"domain_scores_codex":[0.9900081,0.006346752,0.0005427119,0.0009666194,0.001950378,0.0001853286],"domain_scores_gemma":[0.967229,0.02385354,0.001597123,0.004287716,0.002490519,0.0005419776],"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":[0.00001476109,0.00001887588,0.0004626934,0.0001096693,0.00004511606,0.00006808125,0.0001859271,0.01089629,0.0004249081,0.9679336,0.002123393,0.01771666],"study_design_scores_gemma":[0.000007660891,0.00002027546,0.0001787182,0.00003215595,0.000007845362,0.00003488106,0.00004140629,0.07715203,0.0002238301,0.9168972,0.005388597,0.00001555248],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008810242,0.00055671,0.9956677,0.001622842,0.00008185514,0.00002643918,0.00007916721,0.00008219275,0.00100205],"genre_scores_gemma":[0.1217164,0.002372789,0.8697723,0.001518391,0.00153906,0.0005521305,0.0003433927,0.0002145963,0.001970913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01674225,"threshold_uncertainty_score":0.08854252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1792265927896196,"score_gpt":0.3183334695249175,"score_spread":0.1391068767352979,"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."}}