{"id":"W3110521578","doi":"10.1016/j.forsciint.2020.110628","title":"Automated reconstruction of cast-off blood spatter patterns based on Euclidean geometry and statistical likelihood","year":2020,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Center for Statistics and Applications in Forensic Evidence","keywords":"Robustness (evolution); Swing; Computer vision; Artificial intelligence; Computer science; Geometry; Euclidean geometry; 3D reconstruction; Euclidean distance; Mathematics; Algorithm; Computer graphics (images); Engineering; Mechanical engineering","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":[0.0005272095,0.0008082092,0.0007735677,0.002673331,0.0003448764,0.001474818,0.0009619127,0.0007696871,0.002062252],"category_scores_gemma":[0.002026266,0.0006688416,0.0008104657,0.001486037,0.0004572246,0.0007442484,0.001038763,0.0009601168,0.001300481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002744916,"about_ca_system_score_gemma":0.00135504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422879,"about_ca_topic_score_gemma":0.003495307,"domain_scores_codex":[0.9996561,0.00006322953,0.00001505537,0.00006703324,0.0001396826,0.00005896547],"domain_scores_gemma":[0.9993198,0.0001987094,0.00009146062,0.0001205352,0.0002124999,0.00005700232],"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":[0.001089607,0.0001958735,0.016591,0.0003114935,0.0001840386,0.001376605,0.0006584956,0.1609521,0.1350186,0.008127607,0.006812727,0.6686819],"study_design_scores_gemma":[0.00002778,0.00006240891,0.006537851,0.00002487994,0.00003533371,0.0009263916,0.0001176731,0.9643314,0.02152966,0.003975896,0.002383765,0.00004699649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09997796,0.0002777835,0.8954688,0.0001663543,0.0000387127,0.00006661702,0.0003481987,0.002276102,0.001379445],"genre_scores_gemma":[0.520243,0.0004547608,0.4748537,0.0000583311,0.00004854161,0.00006841098,0.0009901955,0.0006384081,0.002644657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002673331,"threshold_uncertainty_score":0.00689894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.016385833530888,"score_gpt":0.2838907312499052,"score_spread":0.2675048977190172,"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."}}