{"id":"W3115967792","doi":"10.1109/csp51677.2021.9357588","title":"Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fingerprint (computing); Artificial intelligence; Term (time); Focus (optics); Convolutional neural network; Machine learning; Pattern recognition (psychology); Fingerprint recognition; Recall; Convolution (computer science); Variety (cybernetics); Psychology; Cognitive psychology; Artificial neural network","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"],"consensus_categories":[],"category_scores_codex":[0.0007445981,0.0003024791,0.0003716763,0.0003248176,0.0002339923,0.0009785203,0.001041921,0.000448185,0.000170774],"category_scores_gemma":[0.0001563429,0.0003222824,0.0003101117,0.0005554493,0.0001000903,0.0003191062,0.00122155,0.0005453686,0.00001545758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664622,"about_ca_system_score_gemma":0.0002634146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005978705,"about_ca_topic_score_gemma":0.00009542162,"domain_scores_codex":[0.9974005,0.0001415803,0.0005681447,0.001086335,0.0004222748,0.0003811321],"domain_scores_gemma":[0.9977276,0.000273995,0.0002161448,0.000932435,0.0006919056,0.0001579734],"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":[0.00003039469,0.000744373,0.001551821,0.0005794085,0.0004245481,0.00003225307,0.0006446548,0.0005550232,0.0009304179,0.005053745,0.1000135,0.8894398],"study_design_scores_gemma":[0.001475518,0.0001074331,0.04984497,0.0005191856,0.0002036613,0.00008240655,0.0002116158,0.9117715,0.01769145,0.01042612,0.004887581,0.002778524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02707323,0.001227068,0.9662771,0.0006409801,0.003013228,0.0008398619,0.00008523761,0.0002777189,0.0005655939],"genre_scores_gemma":[0.8953978,0.0003730019,0.09542438,0.0007328504,0.0003960042,0.0004689262,0.005317568,0.0000315438,0.001857957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9112165,"threshold_uncertainty_score":0.9999229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0480085940930848,"score_gpt":0.27467371184911,"score_spread":0.2266651177560252,"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."}}