{"id":"W3023536793","doi":"10.1016/j.forsciint.2020.110314","title":"Building a ground-truth fingerprint dataset for proficiency testing and research","year":2020,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Forensic Fingerprint Detection Methods","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Royal Canadian Mounted Police","funders":"","keywords":"Computer science; Workflow; Fingerprint (computing); Identification (biology); Process (computing); Crime scene; Quality (philosophy); ENCODE; Variety (cybernetics); Artificial intelligence; Database; Psychology; Biology","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":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.007136573,0.0001086074,0.0001291698,0.0003220113,0.001540006,0.0005465605,0.0009159333,0.00005632256,0.0000514326],"category_scores_gemma":[0.03938581,0.000107667,0.00003493608,0.001606052,0.002947525,0.0006976351,0.0004363676,0.0002681822,0.00001651712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002603442,"about_ca_system_score_gemma":0.0005361083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000758182,"about_ca_topic_score_gemma":0.00006964216,"domain_scores_codex":[0.9967899,0.0001271833,0.0002696168,0.0006857342,0.001533465,0.000594101],"domain_scores_gemma":[0.9966639,0.001802139,0.000109194,0.0001749953,0.0009654956,0.0002842287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001145224,0.00004788529,0.004469657,0.00003454057,0.00001945294,0.00001036354,0.01263217,0.0001509848,0.02770719,0.6444455,0.003477499,0.3068902],"study_design_scores_gemma":[0.001820155,0.001350559,0.02526115,0.0002981141,0.00003720295,0.00006779493,0.01475123,0.2221555,0.03795972,0.4169968,0.2778734,0.001428321],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8795199,0.00003552437,0.07798103,0.02484032,0.004068898,0.001327867,0.0003086782,0.0002429048,0.01167485],"genre_scores_gemma":[0.7365965,0.000002869669,0.2623297,0.0003322787,0.0006388373,0.00004625449,0.000008688854,0.000009293155,0.0000356494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3054619,"threshold_uncertainty_score":0.9997659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2447487091095955,"score_gpt":0.4854128754507934,"score_spread":0.2406641663411979,"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."}}