{"id":"W2028414401","doi":"10.1111/1556-4029.12060","title":"Forensic Considerations for Preprocessing Effects on Clinical <scp>MDCT</scp> Scans","year":2013,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Preprocessor; Scanner; Computer science; Computer vision; Artificial intelligence; Data pre-processing; Raw data; Imaging phantom; Iterative reconstruction; Pattern recognition (psychology); Computer graphics (images); Nuclear medicine; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001350981,0.00014204,0.0004896841,0.0002115414,0.0002831378,0.0001256282,0.000137268,0.0001013766,0.00002429092],"category_scores_gemma":[0.005112056,0.00008751548,0.0003096471,0.0002204047,0.0005135525,0.0003351302,0.00002500591,0.0002519037,0.000009749426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004021402,"about_ca_system_score_gemma":0.0004141832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008043115,"about_ca_topic_score_gemma":0.000005899882,"domain_scores_codex":[0.9982299,0.00004628019,0.000713753,0.0002252728,0.0004828753,0.0003019423],"domain_scores_gemma":[0.9957287,0.002838154,0.0005502815,0.0001661891,0.000508823,0.0002078959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0000708842,0.0004791633,0.2925973,0.0003752858,0.000288515,0.0001250383,0.001341618,0.0001113903,0.003919437,0.01270904,0.3626417,0.3253406],"study_design_scores_gemma":[0.007020537,0.02457693,0.5357431,0.003266491,0.000796315,0.003424534,0.002383442,0.006527114,0.1397529,0.2683911,0.007755063,0.000362463],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863988,0.0002653861,0.003998423,0.004777912,0.0009011657,0.0006162652,0.000001426921,0.00004379766,0.002996835],"genre_scores_gemma":[0.9058146,0.00002769705,0.08980528,0.003308885,0.000639926,0.00001403105,4.661918e-7,0.00001131538,0.0003777384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3548867,"threshold_uncertainty_score":0.6119977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07369656488799144,"score_gpt":0.3917075994693151,"score_spread":0.3180110345813237,"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."}}