{"id":"W3124065190","doi":"10.1016/j.forsciint.2021.110690","title":"A comparison of reverse projection and PhotoModeler for suspect height analysis","year":2021,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Celiac Association; CARE Canada; University of Toronto; Veterans Affairs Canada","funders":"","keywords":"Projection (relational algebra); Measure (data warehouse); Accuracy and precision; Suspect; Statistics; Mathematics; Standard deviation; Point cloud; Computer science; Artificial intelligence; Computer vision; Algorithm; Psychology; Data mining","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":[0.001401454,0.0006305078,0.000588445,0.001699788,0.0005302246,0.001112613,0.0008308471,0.0008195329,0.007560292],"category_scores_gemma":[0.003359405,0.0005363401,0.0005322701,0.0009480394,0.000382273,0.001422054,0.001113249,0.0006177392,0.001663969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002798538,"about_ca_system_score_gemma":0.00085314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001880154,"about_ca_topic_score_gemma":0.003462346,"domain_scores_codex":[0.9985514,0.0003059354,0.00004547801,0.0002282613,0.0007764512,0.00009242314],"domain_scores_gemma":[0.9968182,0.001345477,0.000108144,0.0004542504,0.001187221,0.0000867454],"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":[0.00244655,0.0002580233,0.01417904,0.0006082832,0.0001347063,0.0003324257,0.0005365885,0.004206525,0.3059728,0.002938448,0.003161513,0.6652251],"study_design_scores_gemma":[0.0002167479,0.002622436,0.07976186,0.0001813843,0.0007311925,0.009360909,0.001473295,0.3198484,0.5619984,0.002202644,0.02125454,0.0003481109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3001851,0.003521587,0.6774122,0.0003567711,0.0002664402,0.0002528359,0.0005503291,0.005344864,0.01210996],"genre_scores_gemma":[0.6003169,0.002134789,0.3901448,0.0001392624,0.0000732702,0.00007998092,0.0005223079,0.0005714821,0.006017254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007560292,"threshold_uncertainty_score":0.02529168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06526506648653775,"score_gpt":0.3745662530532974,"score_spread":0.3093011865667597,"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."}}