{"id":"W7133340357","doi":"10.1109/ijcb65343.2025.11410879","title":"2nd Latent in the Wild Fingerprint Recognition Competition","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Fingerprint (computing); Competition (biology); Quality (philosophy); Fingerprint recognition; Pattern recognition (psychology); Latent class model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01807819,0.003487653,0.003508685,0.003538725,0.00254639,0.006097189,0.003560489,0.003183123,0.01682537],"category_scores_gemma":[0.01549819,0.0005428515,0.002074015,0.001820437,0.001199211,0.004057377,0.006905404,0.003757705,0.02018674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002324676,"about_ca_system_score_gemma":0.004148081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008337419,"about_ca_topic_score_gemma":0.01716563,"domain_scores_codex":[0.9846675,0.003039221,0.0006067282,0.002065003,0.007462696,0.002158833],"domain_scores_gemma":[0.9812138,0.001821072,0.0004294037,0.003071211,0.008628285,0.004836162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001606089,0.001023151,0.0031335,0.001163773,0.0002879376,0.0004028701,0.0001735925,0.002134993,0.01086543,0.002093796,0.8188031,0.1583117],"study_design_scores_gemma":[0.0008011152,0.002767956,0.04178919,0.0005699534,0.0002958271,0.002306399,0.0007282164,0.03897661,0.02707804,0.006658215,0.8776495,0.0003789914],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2271178,0.03592192,0.1929553,0.02188865,0.08346646,0.008280196,0.2332614,0.06234636,0.134762],"genre_scores_gemma":[0.2092243,0.004367768,0.08100775,0.005046701,0.005621305,0.003705256,0.5309644,0.005247546,0.154815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01807819,"threshold_uncertainty_score":0.09560776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04006497407302008,"score_gpt":0.2711739512777087,"score_spread":0.2311089772046886,"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."}}