{"id":"W2916105024","doi":"10.1142/s0218001419400214","title":"Objective Identification of Bullets Based on 3D Pattern Matching and Line Counting Scores","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ultra Electronics (Canada)","funders":"","keywords":"Cartridge; Identification (biology); Artificial intelligence; Discriminative model; Computer science; Matching (statistics); Line (geometry); Pattern recognition (psychology); Set (abstract data type); Caliber; Computer vision; Statistics; Mathematics; Engineering; Geometry","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":[0.002066125,0.0007118735,0.0007800333,0.00480558,0.0003012453,0.001427104,0.0007701086,0.0007191737,0.002519287],"category_scores_gemma":[0.006107343,0.0002281288,0.0005643387,0.001861677,0.0007702319,0.001243547,0.001473196,0.0005183072,0.001486392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003646697,"about_ca_system_score_gemma":0.0006251042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001407672,"about_ca_topic_score_gemma":0.00299744,"domain_scores_codex":[0.9977664,0.0001958381,0.0002085111,0.0004635532,0.00119398,0.0001717814],"domain_scores_gemma":[0.995338,0.00136872,0.001242762,0.0004042725,0.00144326,0.0002029242],"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":[0.001471441,0.0003465344,0.1951985,0.001035461,0.0004656095,0.0005736568,0.0004631759,0.01927967,0.269239,0.002147264,0.003766471,0.5060132],"study_design_scores_gemma":[0.00006083869,0.001169021,0.3686238,0.0001602348,0.000259436,0.00294281,0.0006928124,0.4025601,0.2155037,0.002561607,0.005157638,0.0003080329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6746637,0.0004898626,0.3155202,0.00005929717,0.00006492719,0.0004687835,0.002263505,0.00182162,0.004648152],"genre_scores_gemma":[0.8082104,0.0003130088,0.1858179,0.00004382578,0.00002745481,0.0002082467,0.003317548,0.0001595913,0.001902004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00480558,"threshold_uncertainty_score":0.01092684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03517358454341876,"score_gpt":0.3194152317642966,"score_spread":0.2842416472208779,"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."}}