{"id":"W2018196039","doi":"10.1007/s11390-005-0008-2","title":"Online Palmprint Identification System for Civil Applications","year":2005,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Preprocessor; Biometrics; Artificial intelligence; Identification (biology); Process (computing); Computer vision; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Matching (statistics); False positive rate; Interface (matter); Identity (music)","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":[0.001251701,0.00006981529,0.000146755,0.00145691,0.0002369227,0.0002310134,0.00143404,0.00006741495,5.312396e-7],"category_scores_gemma":[0.00005381597,0.00005941985,0.00003594512,0.002745228,0.0002685048,0.0006377501,0.0002118312,0.0001199,0.000005173073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012692,"about_ca_system_score_gemma":0.0002031278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.32977e-7,"about_ca_topic_score_gemma":0.000002513287,"domain_scores_codex":[0.9987772,0.00001184773,0.0004501986,0.0002503622,0.0003380422,0.0001724131],"domain_scores_gemma":[0.9977566,0.00004425043,0.0003934285,0.0003896416,0.001334431,0.00008164115],"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":[0.000001070757,0.0001206917,0.00009394437,0.00001782885,0.000006631586,8.117643e-7,0.00007860579,0.00001427184,0.004521866,0.215392,0.0003184741,0.7794338],"study_design_scores_gemma":[0.001459946,0.0004670108,0.009005557,0.00007622551,0.00003411181,0.001526503,0.000219034,0.5708498,0.04730644,0.02098526,0.3476227,0.0004474377],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714725,0.0002788982,0.9733231,0.008627145,0.0003566127,0.0001688081,0.000002010654,0.00007717984,0.000018969],"genre_scores_gemma":[0.7950646,0.00003156937,0.2046327,0.00009834288,0.0001473125,0.000009963508,4.056714e-7,0.000002053208,0.00001298851],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7789864,"threshold_uncertainty_score":0.2664827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144270102444696,"score_gpt":0.268303194913213,"score_spread":0.2538761846687434,"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."}}