{"id":"W4394064430","doi":"10.2316/j.2024.201-0340","title":"HUMAN IDENTIFICATION USING AI, 65-75. SI","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computational biology; Computer science; Artificial intelligence; Biology; Botany","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009979892,0.001001195,0.0005907208,0.002007725,0.000781607,0.001797012,0.0005913019,0.0008813223,0.1470152],"category_scores_gemma":[0.001931249,0.000400842,0.0003465974,0.001633474,0.0005521675,0.001676977,0.001022274,0.001010464,0.0775953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00058174,"about_ca_system_score_gemma":0.000626419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005570715,"about_ca_topic_score_gemma":0.00739784,"domain_scores_codex":[0.9996707,0.00005965105,0.00002329351,0.00009815616,0.000123695,0.00002456573],"domain_scores_gemma":[0.9990184,0.0002662337,0.00002513641,0.0001783672,0.0004527375,0.0000590452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002760079,0.00009828187,0.001158999,0.000321534,0.0000439064,0.0001213212,0.0002250993,0.001984937,0.02791932,0.01059853,0.1289069,0.8283453],"study_design_scores_gemma":[0.00007035801,0.0003025208,0.01110319,0.0004332143,0.0001248961,0.0008834382,0.0003850269,0.04850305,0.04032551,0.01111814,0.8866436,0.0001070108],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0122891,0.007745661,0.5506794,0.00101325,0.00164775,0.0004108645,0.004007686,0.02221959,0.3999866],"genre_scores_gemma":[0.1031687,0.005205248,0.2070404,0.0004347901,0.0004182093,0.0005711786,0.007105257,0.002378733,0.6736773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1470152,"threshold_uncertainty_score":0.4918147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007451124077263065,"score_gpt":0.2283249663665547,"score_spread":0.2208738422892917,"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."}}