{"id":"W2299691622","doi":"10.1118/1.4939808","title":"Evaluation of a commercial MRI Linac based Monte Carlo dose calculation algorithm with <scp>geant</scp> 4","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Elekta","keywords":"Imaging phantom; Monte Carlo method; Physics; Voxel; Linear particle accelerator; Nuclear medicine; Dosimetry; Beam (structure); Photon; Computational physics; Optics; Mathematics; Medicine; Radiology","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":[0.0005562479,0.0001699758,0.0002576048,0.0000316216,0.00005851436,0.000007522391,0.0001599106,0.00006991989,0.0001094587],"category_scores_gemma":[0.00005768569,0.0001112169,0.00008760118,0.0001693153,0.0001799002,0.0001564447,0.00002319667,0.000144588,0.000003547615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008658648,"about_ca_system_score_gemma":0.0002858422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000992296,"about_ca_topic_score_gemma":0.000005323556,"domain_scores_codex":[0.9979256,0.0001418271,0.000243054,0.0002316027,0.001240525,0.0002173567],"domain_scores_gemma":[0.9987833,0.0002795184,0.0001825314,0.0002903138,0.0003420892,0.0001221935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001042609,0.0002772295,0.009719844,0.00000765789,0.00008690851,8.942754e-7,0.0001521313,0.0002530995,0.000612483,0.0004508304,0.0009407727,0.9874877],"study_design_scores_gemma":[0.02082256,0.001470312,0.03003502,0.001336306,0.000952304,0.000002590212,0.0001064132,0.6656857,0.2332753,0.02816845,0.01734587,0.0007991208],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06295291,0.00004273149,0.9355065,0.0001944013,0.00006506428,0.0003551762,0.00004754402,0.00005127292,0.0007844312],"genre_scores_gemma":[0.987404,0.000005538582,0.01158746,0.00008867809,0.0007066461,0.00008131505,0.0000251945,0.00003552293,0.00006561281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9866886,"threshold_uncertainty_score":0.4535296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710680937707743,"score_gpt":0.3029883071660786,"score_spread":0.2858814977890011,"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."}}