{"id":"W2120507475","doi":"10.1016/j.jvir.2009.04.052","title":"Emerging Technologies Articles","year":2009,"lang":"en","type":"article","venue":"Journal of Vascular and Interventional Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Emerging technologies; Interventional radiology; Variety (cybernetics); Library science; Medical physics; Radiology; Computer science; Artificial intelligence","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":[0.0005198716,0.00006210618,0.0002480819,0.0001745258,0.00003564333,0.00001009635,0.00006305605,0.00005346158,0.00005118868],"category_scores_gemma":[0.0004844855,0.00004418351,0.0002569467,0.00006492819,0.0000934617,0.0000638575,0.00001462642,0.0003081898,0.000001483331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001835139,"about_ca_system_score_gemma":0.00002013356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001412142,"about_ca_topic_score_gemma":1.183725e-7,"domain_scores_codex":[0.9993236,0.00003951087,0.0003182187,0.00007554476,0.0001327039,0.0001104086],"domain_scores_gemma":[0.9996427,0.00003371856,0.0001287067,0.00006433703,0.00007248127,0.00005806371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002657721,0.0005267163,0.04107074,0.00008613992,0.0008077326,0.0004718639,0.0002220635,0.0001045433,0.02684552,0.01933111,0.008654123,0.9016137],"study_design_scores_gemma":[0.009343646,0.009995869,0.7267633,0.001386603,0.0007928684,0.04147841,0.00121949,0.01070926,0.003043797,0.08224255,0.1126068,0.0004174198],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538456,0.0118547,0.01709784,0.0166576,0.0001547715,0.00004084245,2.687118e-7,0.00002257786,0.0003257793],"genre_scores_gemma":[0.9944881,0.0006885759,0.004262404,0.0003833128,0.0001297615,3.373343e-7,0.000001140381,0.000003718338,0.00004268924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9011962,"threshold_uncertainty_score":0.1801751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009796032960949495,"score_gpt":0.3028374812811568,"score_spread":0.2930414483202073,"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."}}