{"id":"W4205306454","doi":"10.1109/access.2022.3144308","title":"Cross-Spectrum Thermal Face Pattern Generator","year":2022,"lang":"lv","type":"article","venue":"IEEE Access","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Taiwan University of Science and Technology; National Taiwan University; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Artificial intelligence; Computer science; Facial recognition system; Face (sociological concept); Computer vision; Thermal; Task (project management); Generator (circuit theory); Pattern recognition (psychology); Image (mathematics); Thermography; Optics; Infrared; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007484336,0.0006695664,0.0004775196,0.0003356335,0.000169252,0.0003495179,0.0008170709,0.0005926376,0.004791657],"category_scores_gemma":[0.001506506,0.0002177818,0.0005092096,0.0002159873,0.000459175,0.000428314,0.0007708902,0.0007685203,0.001059826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003855851,"about_ca_system_score_gemma":0.0003127941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009225466,"about_ca_topic_score_gemma":0.0008908525,"domain_scores_codex":[0.9996914,0.00006453377,0.000008582752,0.00009866442,0.00009301047,0.00004390814],"domain_scores_gemma":[0.9996446,0.0001250639,0.0000292955,0.00008826131,0.00009158278,0.00002107915],"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":[0.0006031436,0.0002362854,0.001643519,0.0001425994,0.00009452999,0.0003417654,0.00006790111,0.676968,0.05045764,0.01104794,0.00657994,0.2518167],"study_design_scores_gemma":[0.0000095507,0.00005804818,0.0004393515,0.000005334204,0.000008185079,0.0001241708,0.000004854774,0.9861118,0.01088099,0.001731351,0.0006177364,0.000008782346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1139971,0.0003092474,0.874405,0.0002602958,0.000221318,0.0002359666,0.0003924933,0.002033942,0.008144712],"genre_scores_gemma":[0.8868202,0.0001262493,0.1009287,0.0002989771,0.00003980613,0.0002383687,0.0006517808,0.0001374739,0.01075846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004791657,"threshold_uncertainty_score":0.01602966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03301523288086645,"score_gpt":0.2917616735868653,"score_spread":0.2587464407059988,"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."}}