{"id":"W4401726091","doi":"10.1109/tbiom.2024.3446964","title":"Large-Scale Fully-Unsupervised Re-Identification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biometrics Behavior and Identity Science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Identification (biology); Scale (ratio); Computer science; Artificial intelligence; Geography; Cartography; Biology; Ecology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001561752,0.0001475849,0.0001176147,0.002928331,0.0008419731,0.00248743,0.001009433,0.00007553378,0.00004026679],"category_scores_gemma":[0.00003842184,0.0001428216,0.00007204837,0.01146922,0.0002257058,0.003824909,0.00001651897,0.0002868018,0.0002277451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001148269,"about_ca_system_score_gemma":0.000120981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007649636,"about_ca_topic_score_gemma":0.00004924471,"domain_scores_codex":[0.9975744,0.00005078471,0.0002985603,0.0008598499,0.0008851761,0.0003311621],"domain_scores_gemma":[0.9987889,0.0000848854,0.00005389575,0.0007121352,0.0001648416,0.0001953539],"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":[0.00000951843,0.001246711,0.002608287,0.0001426849,0.00001603586,0.0000316691,0.003082444,0.0001554035,0.1836248,0.01373018,0.0004969166,0.7948554],"study_design_scores_gemma":[0.0009948208,0.0005584882,0.3321965,0.0001463636,0.0002388689,0.0001438721,0.0009505973,0.5657693,0.0813582,0.001499393,0.01461358,0.001529974],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.151177,0.0001629628,0.8457356,0.000440655,0.001806052,0.0001653114,0.00005077983,0.0003254637,0.0001362343],"genre_scores_gemma":[0.9948553,0.0002514218,0.004144791,0.0000561805,0.00002486879,0.00004122556,0.000006770851,0.000008870392,0.0006105297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8436784,"threshold_uncertainty_score":0.9985481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969761813694448,"score_gpt":0.323513884743391,"score_spread":0.2938162666064465,"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."}}