{"id":"W4388460723","doi":"10.1080/00085030.2023.2278911","title":"Intra- and inter-rater reliability of a manual codification system for footwear impressions: first lessons learned from the development of a footwear database for forensic intelligence purposes","year":2023,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; International Centre for Comparative Criminology","funders":"","keywords":"Reliability (semiconductor); Law enforcement; Forensic science; Crime scene; Computer science; Applied psychology; Test (biology); Psychology; Kappa; Inter-rater reliability; Cohen's kappa; Artificial intelligence; Law; Machine learning; Criminology; Mathematics; Developmental psychology; Political science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002112163,0.0001197139,0.000212258,0.00007574782,0.0004970682,0.00003878418,0.000488985,0.00009481858,0.000003319607],"category_scores_gemma":[0.0005726781,0.00008683291,0.0001474671,0.0003021271,0.001573366,0.00001806348,0.0001355686,0.0001085121,3.939714e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008197557,"about_ca_system_score_gemma":0.001548148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129144,"about_ca_topic_score_gemma":0.006974047,"domain_scores_codex":[0.9984484,0.00003154571,0.0004932608,0.0003365573,0.0003006327,0.0003895917],"domain_scores_gemma":[0.998269,0.000168947,0.0002575419,0.0003791646,0.0006918851,0.0002334693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000420434,0.00006313379,0.009014806,0.0008438776,0.0002670182,0.000001027544,0.01347495,0.0002905607,0.5655268,0.0007392777,0.03164908,0.3777091],"study_design_scores_gemma":[0.0005496559,0.0004002439,0.008907927,0.0004092678,0.00004111363,0.00001280121,0.02086389,0.004774632,0.9567008,0.001440767,0.005713319,0.0001855877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975092,0.0003011727,0.0214187,0.001948612,0.0001983199,0.000578107,0.0004458005,0.000003515873,0.00001377038],"genre_scores_gemma":[0.9578127,0.0002336022,0.04167533,0.00004125419,0.00007844606,0.00003795497,0.00007281112,0.00001080054,0.00003705834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.391174,"threshold_uncertainty_score":0.5797132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07127048238383535,"score_gpt":0.340247174225288,"score_spread":0.2689766918414527,"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."}}