{"id":"W4400811103","doi":"10.1109/csci62032.2023.00197","title":"Thermal Face Image Classification Using Deep Learning Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Artificial intelligence; Computer science; Face (sociological concept); Contextual image classification; Facial recognition system; Pattern recognition (psychology); Image (mathematics); Deep learning; Computer vision","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":[0.0001973319,0.0000561312,0.00006251964,0.0001459066,0.0001255223,0.0001447828,0.0002139564,0.00003036033,0.00007635461],"category_scores_gemma":[0.00002380136,0.00004880733,0.00004916464,0.0007156635,0.00001666272,0.0003561469,0.00008346428,0.00007732199,0.0004664038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001781473,"about_ca_system_score_gemma":0.00001071576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001386342,"about_ca_topic_score_gemma":0.000002083769,"domain_scores_codex":[0.9994137,0.00005071583,0.00009713276,0.0001780553,0.0001247732,0.0001356067],"domain_scores_gemma":[0.9997027,0.00002765912,0.00003854681,0.0001469773,0.00005029735,0.00003384489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[5.727048e-7,0.00001594901,0.000413602,0.000006348957,0.00001379974,0.000005718316,0.0003801476,0.0005723489,0.5744323,0.003230024,0.0001322828,0.4207969],"study_design_scores_gemma":[0.00002803866,0.000006738623,0.001044011,0.000004880855,0.000003561442,0.000001193531,0.0002688075,0.9472869,0.05026449,0.0001525671,0.0008573112,0.00008146947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03425949,0.000006475007,0.9550689,0.0007461497,0.00002476682,0.00004242808,1.451542e-7,0.00113402,0.008717665],"genre_scores_gemma":[0.8611286,0.00002918026,0.1367513,0.0001428839,0.00002725348,0.000007131067,0.000006166549,0.000007329431,0.001900195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9467146,"threshold_uncertainty_score":0.5994833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416828411686801,"score_gpt":0.2989944235626212,"score_spread":0.2548261394457532,"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."}}