{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006336035,0.0004645148,0.0004367715,0.0001399362,0.001721642,0.002735001,0.004316972,0.00009927884,0.003505535],"category_scores_gemma":[0.00002002927,0.0004727142,0.000269003,0.0008684644,0.0001576148,0.001725842,0.002400493,0.0006421471,0.0002384832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964394,"about_ca_system_score_gemma":0.0002586052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007025163,"about_ca_topic_score_gemma":0.00005890482,"domain_scores_codex":[0.9958009,0.0006512841,0.000542548,0.001177621,0.0008552171,0.0009724421],"domain_scores_gemma":[0.9979702,0.0001302322,0.0003368813,0.001216166,0.0001072919,0.0002392139],"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":[0.0000751326,0.0005618445,0.02259777,0.0000477612,0.0003090945,0.0004625056,0.00280921,0.7733121,0.01173652,0.0002987901,0.02777526,0.160014],"study_design_scores_gemma":[0.001508391,0.0004391745,0.02875759,0.00002704002,0.00007238571,0.00005112762,0.0001228684,0.8139513,0.08832107,0.0003526265,0.06474692,0.001649482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.339003,0.001300858,0.6423689,0.003200921,0.01210493,0.0004811643,0.0001793135,0.0001491701,0.001211779],"genre_scores_gemma":[0.9913883,0.00004595807,0.0003432858,0.003054724,0.002223165,0.0001046628,0.00001099253,0.0000567639,0.002772155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6523853,"threshold_uncertainty_score":0.9997724,"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."}}