{"id":"W1963503100","doi":"10.1016/j.compmedimag.2006.05.001","title":"Medical imaging and graphics in SIBGRAPI/SIACG","year":2006,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Connaught Fund; University of Toronto; University of Manchester; University of East Anglia","keywords":"Computer graphics (images); Computer science; Graphics; Medical imaging; Computer graphics; Computer vision; Artificial intelligence; Medical physics; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001705821,0.000910377,0.000920336,0.003565811,0.001037297,0.003517644,0.0008516358,0.001127589,0.2229312],"category_scores_gemma":[0.002223169,0.0006226446,0.0004800164,0.003576672,0.001100668,0.002754345,0.002195468,0.001701911,0.1261186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008894865,"about_ca_system_score_gemma":0.001307093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002761178,"about_ca_topic_score_gemma":0.004018234,"domain_scores_codex":[0.9991937,0.0000898073,0.00008227705,0.0001428515,0.000439807,0.00005154102],"domain_scores_gemma":[0.998244,0.0003149211,0.00007721117,0.0004455548,0.0006125584,0.0003055935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001492792,0.00008104561,0.0007241035,0.0003303989,0.00001924697,0.0004436503,0.0002468591,0.001668342,0.006544401,0.06747333,0.2038633,0.718456],"study_design_scores_gemma":[0.00001872039,0.00003229884,0.001101705,0.0001472431,0.00001362305,0.00122708,0.00004685558,0.005468173,0.004374518,0.0107639,0.97679,0.00001596733],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0118453,0.01699286,0.1347125,0.004405129,0.006220878,0.000228482,0.003335756,0.01963685,0.8026221],"genre_scores_gemma":[0.0442275,0.008502978,0.06749767,0.0006647783,0.001913237,0.0001367941,0.005084066,0.006047375,0.8659257],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2229312,"threshold_uncertainty_score":0.7457792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079087593243021,"score_gpt":0.2942012588635027,"score_spread":0.2834103829310725,"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."}}