{"id":"W1963044959","doi":"10.1002/cem.2636","title":"Constrained kernelized partial least squares","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada","keywords":"Partial least squares regression; Nonlinear system; Kernel (algebra); Latent variable; Noise (video); Mathematical optimization; Computer science; Variable (mathematics); Mathematics; Kernel method; Algorithm; Artificial intelligence; Machine learning; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006850939,0.00008327664,0.0002066708,0.0005245252,0.00004888541,0.0001260628,0.0004704752,0.00006335843,0.00004985933],"category_scores_gemma":[0.0009773062,0.00006452102,0.0001203402,0.001033069,0.00003137109,0.0004213859,0.00006294719,0.0001792571,0.00004332504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061198,"about_ca_system_score_gemma":0.00005933952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.637761e-7,"about_ca_topic_score_gemma":1.00247e-7,"domain_scores_codex":[0.9988419,0.00005628508,0.0003720369,0.0001036633,0.0004616241,0.0001644996],"domain_scores_gemma":[0.9988047,0.0002069626,0.0003607173,0.0001635458,0.0003189873,0.0001450282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001265724,0.0005007158,0.003166355,0.00007370295,0.00009343756,0.00008146159,0.0006650017,0.0003200467,0.04593697,0.008337541,0.04827318,0.892425],"study_design_scores_gemma":[0.01268056,0.002257928,0.006963612,0.000444475,0.0001097523,0.001366325,0.0003497798,0.09620534,0.4873133,0.023315,0.3678121,0.001181824],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1318836,0.0001844299,0.8608719,0.00227132,0.0009774736,0.00004514187,0.000001083733,0.00003693439,0.003728201],"genre_scores_gemma":[0.9727079,0.00004017816,0.02650317,0.0003714566,0.0002691628,5.945534e-7,4.965083e-7,0.000004863371,0.000102151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8912432,"threshold_uncertainty_score":0.2631091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519331425504999,"score_gpt":0.2429381963019769,"score_spread":0.2277448820469269,"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."}}