{"id":"W3159134235","doi":"10.1109/icpr48806.2021.9412168","title":"A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Canonical correlation; Discriminant; Computer science; Feature (linguistics); Face (sociological concept); Representation (politics); Contextual image classification; Feature extraction; Correlation; Class (philosophy); Facial recognition system; Image (mathematics); Data mining; 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":[0.0008733036,0.001136806,0.0009079545,0.001139168,0.0004147909,0.0009623469,0.001381004,0.0009027649,0.001985942],"category_scores_gemma":[0.002162721,0.0003051179,0.0007107445,0.001346853,0.0006977015,0.001654839,0.001327465,0.001416888,0.000590876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122432,"about_ca_system_score_gemma":0.00129161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007073213,"about_ca_topic_score_gemma":0.007681397,"domain_scores_codex":[0.9995258,0.0001212031,0.00001987021,0.0001326953,0.0001361797,0.00006422208],"domain_scores_gemma":[0.9993954,0.0001496667,0.00006439068,0.00009041622,0.0002509498,0.00004930418],"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":[0.0003111656,0.000251929,0.004404077,0.0002005761,0.0001937322,0.0001574837,0.0001427046,0.3465339,0.01596601,0.04951674,0.01517369,0.5671479],"study_design_scores_gemma":[0.000004361771,0.00003267581,0.000269749,0.000006700827,0.0000161357,0.00002587232,0.000008542565,0.9918991,0.001308604,0.005663662,0.0007563588,0.000008192818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02626703,0.0007003039,0.9691491,0.0004020905,0.00008129188,0.0000691154,0.000256254,0.0006951218,0.002379703],"genre_scores_gemma":[0.6846325,0.001002539,0.3035813,0.0005845183,0.0001749354,0.0003316865,0.001850741,0.0001839496,0.007657927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007073213,"threshold_uncertainty_score":0.01406413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984646608646367,"score_gpt":0.3087110716101136,"score_spread":0.2688646055236499,"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."}}