{"id":"W3026922903","doi":"10.1088/1361-6560/ab9413","title":"Molière maximum likelihood proton path estimation approximated by cubic Bézier curve for scatter corrected proton CT reconstruction","year":2020,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiation Therapy and Dosimetry","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Monte Carlo method; Gaussian; Scattering; Stopping power; Proton; Gaussian process; Estimator; Energy (signal processing); Physics; Computational physics; Path (computing); Mathematics; Algorithm; Computer science; Optics; Detector; Statistics; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530998,0.0008196622,0.0009964771,0.0009343891,0.000356624,0.00141979,0.001290236,0.001224346,0.003406149],"category_scores_gemma":[0.004653502,0.000750747,0.001012038,0.001014693,0.000502944,0.00107892,0.001045729,0.001289377,0.001163637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175343,"about_ca_system_score_gemma":0.002217075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005394037,"about_ca_topic_score_gemma":0.004152618,"domain_scores_codex":[0.9992581,0.0002280644,0.00003112281,0.00009660141,0.0003414984,0.00004461616],"domain_scores_gemma":[0.9986619,0.0008245114,0.0001303789,0.0001149643,0.0002174469,0.00005074958],"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":[0.000253064,0.00003586029,0.0006920279,0.0001769847,0.00004815888,0.0001413847,0.000123618,0.8578802,0.01028721,0.01820317,0.002230103,0.1099282],"study_design_scores_gemma":[0.000004729103,0.00001033995,0.00006655092,0.000006391731,0.000002707728,0.00005717423,0.000003229493,0.9952973,0.001834037,0.001917278,0.000790993,0.000009330349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002062727,0.00009154282,0.9969543,0.00003143173,0.000003955719,0.00001478824,0.00003295155,0.0004993352,0.0003089358],"genre_scores_gemma":[0.09633704,0.0002890831,0.9001669,0.00004385347,0.00001289178,0.0001220141,0.0003414909,0.0006596377,0.002027175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005394037,"threshold_uncertainty_score":0.01139468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024984776608861,"score_gpt":0.3590563554601688,"score_spread":0.2565578777992827,"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."}}