{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004495755,0.000108541,0.0002649405,0.0002193267,0.0004893841,0.0006359677,0.001840187,0.00005514702,0.00003346948],"category_scores_gemma":[0.001016191,0.00009637192,0.00004963039,0.0001206692,0.0001058722,0.000432426,0.00005468857,0.0001492945,0.00001378646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000467878,"about_ca_system_score_gemma":0.00197602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001212999,"about_ca_topic_score_gemma":0.01682239,"domain_scores_codex":[0.9988776,0.00004474488,0.0003697036,0.0002135354,0.0002054117,0.0002890347],"domain_scores_gemma":[0.997407,0.0002464634,0.0003459413,0.0009692077,0.0003311725,0.0007002683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001080025,0.00015637,0.03880911,0.0001641792,0.001507384,0.002668073,0.0006586373,0.003225002,0.00008154342,0.07623051,0.6587548,0.2176363],"study_design_scores_gemma":[0.001786871,0.0003963555,0.03291563,0.0001263592,0.0007413856,0.00004825192,0.00004682098,0.8011753,0.0001711369,0.0151385,0.1469736,0.0004797554],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001032947,0.00005903784,0.9916512,0.001178392,0.000924091,0.00008160811,0.005019485,0.000004490484,0.0000487621],"genre_scores_gemma":[0.420219,0.00001279851,0.5785587,0.0005435881,0.0003811596,0.000002882671,0.0002253794,0.00001158791,0.00004486914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7979503,"threshold_uncertainty_score":0.9387291,"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."}}