{"id":"W1983704191","doi":"10.1118/1.3120286","title":"Fast convolution‐superposition dose calculation on graphics hardware","year":2009,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Varian Medical Systems; Nvidia","keywords":"Graphics processing unit; Computer science; Kernel (algebra); Computational science; Massively parallel; Parallel computing; Porting; Central processing unit; Convolution (computer science); Graphics; CUDA; Computer hardware; Software; Artificial intelligence; Artificial neural network; Mathematics","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":[0.0005738635,0.0005001826,0.0005852852,0.0004592928,0.0003874069,0.0007646446,0.001005168,0.0004741184,0.004385265],"category_scores_gemma":[0.002468919,0.0003470112,0.0006672935,0.000945701,0.0002975017,0.0007810076,0.0006463924,0.000610554,0.000741365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008850663,"about_ca_system_score_gemma":0.0009110811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004646049,"about_ca_topic_score_gemma":0.003289276,"domain_scores_codex":[0.9995801,0.00008604952,0.00003604378,0.00003992583,0.000226858,0.00003103703],"domain_scores_gemma":[0.9989605,0.0004272922,0.0000545713,0.0002302565,0.0002938934,0.00003354981],"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":[0.0009431136,0.0001186448,0.00429764,0.0002646789,0.0001787586,0.0004182538,0.000411767,0.4452603,0.09272277,0.02670998,0.009649667,0.4190244],"study_design_scores_gemma":[0.00005090047,0.00008617536,0.0009532555,0.00001150041,0.00002092133,0.0001614562,0.00001533795,0.9599852,0.02915082,0.003676035,0.005867311,0.00002108256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06020959,0.0002575206,0.9271283,0.0001836059,0.00009550215,0.00008605843,0.0001501561,0.005302842,0.006586384],"genre_scores_gemma":[0.405712,0.0001789998,0.5895552,0.0001023328,0.00002157164,0.0000839011,0.0003902199,0.0005571311,0.003398594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004646049,"threshold_uncertainty_score":0.01467019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009707401031026281,"score_gpt":0.2821215648025336,"score_spread":0.2724141637715073,"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."}}