{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001771225,0.00009545915,0.00009824576,0.00006393107,0.0001117231,0.0001002519,0.0003253686,0.00005862056,0.00001181131],"category_scores_gemma":[0.00002039578,0.00007026075,0.00003491153,0.0001804266,0.00003078346,0.00086269,0.0002188666,0.0001162593,0.0001511015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003818365,"about_ca_system_score_gemma":0.0001252586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006783823,"about_ca_topic_score_gemma":0.000002999388,"domain_scores_codex":[0.9989158,0.00006636496,0.0001141118,0.0002892668,0.0003736086,0.0002408384],"domain_scores_gemma":[0.999374,0.00001151834,0.00003480526,0.0002769083,0.0001396641,0.0001630484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000519303,0.0006185147,0.0007664605,0.00007052332,0.0000406573,0.00006318889,0.01408861,0.3977915,0.1396956,0.01892323,0.02462677,0.4027956],"study_design_scores_gemma":[0.0002735124,0.00003003841,0.00002331764,0.00007258414,0.000001389109,0.000005607031,0.0002176704,0.9239085,0.0735293,0.001664654,0.000167077,0.0001063986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1973374,0.00004318647,0.7980826,0.0004446245,0.0001541828,0.0000895181,0.000001354733,0.0001212041,0.003726007],"genre_scores_gemma":[0.9586238,0.000004455692,0.04071858,0.0001803237,0.00005907641,0.000005356176,0.000001531827,0.000005932358,0.0004009797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7612864,"threshold_uncertainty_score":0.286515,"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."}}