{"id":"W4406716867","doi":"10.1088/1361-6560/adad2d","title":"Proton arc therapy plan optimization with energy layer pre-selection driven by organ at risk sparing and delivery time","year":2025,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Waalse Gewest","keywords":"Proton therapy; Selection (genetic algorithm); Proton; Layer (electronics); Energy (signal processing); Computer science; Nuclear engineering; Medicine; Materials science; Artificial intelligence; Statistics; Nuclear physics; Physics; Mathematics; Nanotechnology; Engineering","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":[0.00007277545,0.00009758613,0.0001458567,0.00005906813,0.00004380802,0.000004072646,0.00003342928,0.00005739488,0.000006521241],"category_scores_gemma":[0.000009617047,0.00007428642,0.000004938209,0.000162961,0.00005430651,0.00003966191,0.00001168575,0.0001144248,2.52629e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005578807,"about_ca_system_score_gemma":0.00001057641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009362294,"about_ca_topic_score_gemma":0.00004483201,"domain_scores_codex":[0.9995607,0.00004527484,0.00009424184,0.0001417297,0.0000294973,0.0001285712],"domain_scores_gemma":[0.9997891,0.00008892931,0.00002977678,0.00005809675,0.00001598057,0.00001807916],"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":[0.0001522243,0.00003072494,0.06889403,0.00005870847,0.0001320943,5.680187e-7,0.0003663703,0.8393634,0.04907601,0.000654902,0.000724169,0.04054679],"study_design_scores_gemma":[0.001120776,0.0003728988,0.00145055,0.00004715988,0.0000182015,0.000002924126,0.000007713832,0.9802775,0.01400952,0.0007311138,0.001849682,0.0001119298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9442899,0.00119428,0.05364106,0.00007832108,0.00004616561,0.0002422671,0.000002439685,0.00006380263,0.0004418114],"genre_scores_gemma":[0.9959429,0.003442516,0.0002925936,0.00009313574,0.00007833863,0.00004466583,0.00005384538,0.00001190661,0.00004011303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1409141,"threshold_uncertainty_score":0.3029312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780191747017341,"score_gpt":0.2525883707807293,"score_spread":0.2347864533105559,"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."}}