{"id":"W2093683494","doi":"10.1117/12.467119","title":"Convex geometry for rapid tissue classification in MRI","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voxel; Computer science; Heuristics; Linear programming; Artificial intelligence; Pentium; Pattern recognition (psychology); Point (geometry); Algorithm; Mathematical optimization; Mathematics; Geometry","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":[0.001345642,0.001313974,0.001567045,0.001418022,0.0006209657,0.001864348,0.001848953,0.001128981,0.004520567],"category_scores_gemma":[0.004964704,0.001023202,0.001336955,0.001285229,0.001361194,0.001624982,0.002420275,0.002360811,0.003315735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088474,"about_ca_system_score_gemma":0.001025843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003452387,"about_ca_topic_score_gemma":0.003443038,"domain_scores_codex":[0.9988549,0.0003893998,0.00004995729,0.000170798,0.0004575976,0.00007726622],"domain_scores_gemma":[0.998204,0.001013651,0.0001547582,0.0002433099,0.0003054949,0.00007875459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001207829,0.00004500752,0.0004279769,0.0002124723,0.00005220959,0.0001557215,0.0001219777,0.6958628,0.01073163,0.08760761,0.00844861,0.1962133],"study_design_scores_gemma":[0.000005909441,0.00001530118,0.00007019633,0.000006664451,0.000003431465,0.00003448017,0.000008490335,0.9735913,0.001120885,0.02288448,0.002249228,0.000009622388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001346482,0.0001272013,0.9972783,0.0001162357,0.0000184552,0.000023816,0.0000464362,0.0003965266,0.0006465466],"genre_scores_gemma":[0.06906021,0.0005323035,0.9265081,0.0001382696,0.0001292488,0.0002553369,0.0004345662,0.0005455387,0.002396314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004520567,"threshold_uncertainty_score":0.01512277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470156404752056,"score_gpt":0.2757158896385599,"score_spread":0.2510143255910394,"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."}}