{"id":"W2056913245","doi":"10.1002/mrm.20956","title":"Resolution and SNR effects on carotid plaque classification","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Heart and Stroke Foundation of Canada","keywords":"Weighting; Pattern recognition (psychology); Artificial intelligence; Robustness (evolution); Segmentation; Carotid endarterectomy; Image resolution; Resolution (logic); Computer science; Mathematics; Nuclear medicine; Carotid arteries; Medicine; Radiology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003506813,0.0005279995,0.0005003268,0.0007005131,0.0003154513,0.0008176628,0.0002123285,0.000809406,0.0006620835],"category_scores_gemma":[0.03127918,0.0004458238,0.0003440423,0.0003675352,0.0005460916,0.00104577,0.0005539007,0.00042128,0.0005326457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003024046,"about_ca_system_score_gemma":0.0001368984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006983734,"about_ca_topic_score_gemma":0.0006176655,"domain_scores_codex":[0.9985058,0.0006706773,0.0001385914,0.0002260979,0.0003453458,0.0001135104],"domain_scores_gemma":[0.9815144,0.01531942,0.0009860143,0.0007412653,0.001292694,0.000146231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.007856213,0.000188493,0.07322872,0.0006133271,0.000391977,0.00125805,0.001279112,0.04959025,0.6381007,0.0009927764,0.000638233,0.2258622],"study_design_scores_gemma":[0.00009857239,0.002225413,0.1369286,0.0001266354,0.0006425091,0.004808609,0.0003677514,0.2004484,0.649717,0.00246124,0.002028108,0.0001471262],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9537606,0.00203051,0.04166701,0.0001921997,0.00004012765,0.00002371411,0.00007109775,0.0002813142,0.001933463],"genre_scores_gemma":[0.975854,0.0004502953,0.02278675,0.00008232495,0.00003497332,0.00001219797,0.0001280602,0.00007443252,0.000576836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003506813,"threshold_uncertainty_score":0.01854599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009724151468943593,"score_gpt":0.2448278905084775,"score_spread":0.2351037390395339,"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."}}