{"id":"W2343281977","doi":"10.1109/vcip.2015.7457912","title":"Active appearance model search using partial least squares regression","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Partial least squares regression; Canonical correlation; Covariance matrix; Latent variable; Covariance; Mathematics; Regression analysis; Pattern recognition (psychology); Context (archaeology); Computer science; Active appearance model; Artificial intelligence; Statistics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000921284,0.001184952,0.001740523,0.0008964126,0.0004591286,0.001046781,0.002100001,0.001420845,0.002471123],"category_scores_gemma":[0.002982631,0.0007988856,0.001380936,0.001061626,0.0006425942,0.001327145,0.001312026,0.001546516,0.00159169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003745804,"about_ca_system_score_gemma":0.001083992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00301292,"about_ca_topic_score_gemma":0.003040366,"domain_scores_codex":[0.9992613,0.000211637,0.00002807011,0.0001966447,0.0002440803,0.00005821533],"domain_scores_gemma":[0.9989743,0.0004672621,0.0001022074,0.0001338461,0.0002684706,0.0000539374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001705886,0.0001354521,0.0008651874,0.0001398496,0.0001414892,0.0001425892,0.0001230584,0.5469223,0.02188529,0.01006036,0.005139831,0.4142741],"study_design_scores_gemma":[0.000004838846,0.00001305674,0.0000423591,0.000001825192,0.000004111742,0.00001894248,0.000003287302,0.9976834,0.0009842846,0.000882703,0.0003571606,0.000004053218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003324434,0.00007006879,0.9956392,0.00003278816,0.00001654149,0.00001372338,0.00001339735,0.0005253599,0.0003644007],"genre_scores_gemma":[0.2014525,0.000159303,0.7933322,0.0001612801,0.00005708655,0.0002013797,0.0003083913,0.0003938482,0.003933989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00301292,"threshold_uncertainty_score":0.008266687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498222833119992,"score_gpt":0.3404659226742698,"score_spread":0.1906436393622706,"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."}}