{"id":"W4391917036","doi":"10.1007/978-3-031-55315-8_5","title":"Preoperative Planning of Pipeline Embolization Device Sizing Using Finite Element Method","year":2024,"lang":"en","type":"article","venue":"Lecture notes in computational vision and biomechanics","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Siemens Healthineers","keywords":"Finite element method; Pipeline (software); Sizing; Computer science; Engineering; Structural engineering; Programming language","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":[0.0001594097,0.0001348764,0.0001576309,0.0002357208,0.00003490238,0.00004989723,0.00004730757,0.00009985396,0.000008731003],"category_scores_gemma":[0.00006308289,0.0001145989,0.00002938196,0.000448523,0.00000895249,0.00006752418,0.00002752384,0.0001647438,3.880681e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000422122,"about_ca_system_score_gemma":0.00001775261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006870781,"about_ca_topic_score_gemma":0.000002608274,"domain_scores_codex":[0.9993129,0.00002406845,0.0002487988,0.0001461381,0.0001504731,0.0001176348],"domain_scores_gemma":[0.9994105,0.0004493422,0.00001888801,0.00005019879,0.00004103646,0.00003001134],"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":[0.000003058851,0.000005313294,0.000003362568,0.0001655674,0.000013599,0.000002600494,0.0002922259,0.9427575,0.01447014,0.00133447,0.000002636855,0.0409495],"study_design_scores_gemma":[0.0001125444,0.00003324585,0.00005516297,0.0002659759,0.00001189893,0.000008840996,0.00001534033,0.9744475,0.005505014,0.01925238,0.0001707584,0.0001212776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0197227,0.002275245,0.9774861,0.00007085528,0.000208301,0.00008934044,0.00001221608,0.0001247791,0.00001047673],"genre_scores_gemma":[0.8736166,0.00002625591,0.1262269,0.00004121467,0.00003794371,0.000001746121,0.0000327937,0.00001592566,6.263992e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8538939,"threshold_uncertainty_score":0.4673207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171826213397331,"score_gpt":0.3070496426545118,"score_spread":0.2898670213147788,"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."}}