{"id":"W2747059896","doi":"10.4018/ijcini.2017070101","title":"Biometric Pattern Recognition from Social Media Aesthetics","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Biometrics; Hyperplane; Social media; Artificial intelligence; Feature (linguistics); Feature vector; Visualization; Support vector machine; Graph; k-nearest neighbors algorithm; Pattern recognition (psychology); Feature selection; Decision tree; Machine learning; Information retrieval; World Wide Web; 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":[0.0003300505,0.0001168784,0.0001621621,0.0003565567,0.0001855763,0.0007936895,0.00122833,0.00006880266,0.00002006995],"category_scores_gemma":[0.0009801574,0.00009342005,0.0000927143,0.0001197263,0.0001721116,0.001764311,0.0002476397,0.0002857288,0.00002535477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003645334,"about_ca_system_score_gemma":0.00004615645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001145625,"about_ca_topic_score_gemma":0.000002297448,"domain_scores_codex":[0.9985958,0.00002550668,0.0005990538,0.00008184143,0.0005826399,0.0001151095],"domain_scores_gemma":[0.9964072,0.0004049135,0.001138051,0.0001087299,0.001874899,0.00006622182],"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":[0.00002141979,0.00002826766,0.0001819003,0.000002934632,0.0000483978,0.0000149905,0.001525675,5.29422e-8,0.00007154235,0.0005885119,0.00003523255,0.997481],"study_design_scores_gemma":[0.003268428,0.0009414335,0.1508467,0.002603569,0.0002499244,0.001445789,0.007284674,0.1160802,0.4863848,0.222195,0.006493838,0.002205618],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08090584,0.0002946776,0.9156514,0.001188613,0.00136766,0.00006238992,0.00005144461,0.0000227939,0.0004551154],"genre_scores_gemma":[0.9879477,0.0009570044,0.01052027,0.0003095078,0.0002370882,0.000001342903,0.00001476874,0.000003985554,0.000008302312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9952754,"threshold_uncertainty_score":0.7653565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652896330278132,"score_gpt":0.3220701723805531,"score_spread":0.2755412090777718,"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."}}