{"id":"W3001043714","doi":"10.1088/2057-1976/ab6e15","title":"Feasibility of energy adaptive angular meshing for perpendicular and parallel magnetic fields in a grid based Boltzmann solver","year":2020,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Solver; Grid; Boltzmann constant; Computational science; Perpendicular; Computer science; Physics; Magnetic field; Energy (signal processing); Computational physics; Geometry; Mathematics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006269944,0.0001642427,0.0002832962,0.00003320424,0.00001938338,0.00001014955,0.0001416906,0.00006066578,0.00002140543],"category_scores_gemma":[0.00001164009,0.0001626553,0.0000892689,0.0001206222,0.0000655217,0.000089556,0.00004253488,0.0001283778,7.242382e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001805971,"about_ca_system_score_gemma":0.00002429801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005222764,"about_ca_topic_score_gemma":9.096095e-8,"domain_scores_codex":[0.9991417,0.00001635275,0.0002074569,0.0002713782,0.0001492254,0.0002138798],"domain_scores_gemma":[0.9995522,0.00009451237,0.00005261125,0.000155541,0.00002444329,0.0001206644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005342943,0.001159249,0.007760791,0.0008447173,0.000276939,0.00002370873,0.002282734,0.05012456,0.8540048,0.05056788,0.00258591,0.02983444],"study_design_scores_gemma":[0.00413548,0.0008446049,0.001411077,0.000291191,0.00006127229,5.132601e-7,0.00009880833,0.9060987,0.01941791,0.005753085,0.06100266,0.0008847538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02045848,0.0002929676,0.97875,0.00009920051,0.00004974994,0.0002210115,0.00006841065,0.00005240191,0.000007818565],"genre_scores_gemma":[0.7528832,0.00000406911,0.2465462,0.00006732793,0.0003463031,0.00008640986,0.000038957,0.00002499404,0.000002483187],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8559741,"threshold_uncertainty_score":0.6632891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426911460022871,"score_gpt":0.2436705156039182,"score_spread":0.2294014010036895,"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."}}