{"id":"W2473686228","doi":"10.1118/1.4956725","title":"SU‐F‐T‐540: Comprehensive Fluence Delivery Optimization with Multileaf Collimation","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Calgary","funders":"","keywords":"Multileaf collimator; Computer science; Collimated light; Fluence; Algorithm; Smoothing; Mean squared error; Mathematics; Optics; Computer vision; Linear particle accelerator; Physics; Beam (structure); Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004836508,0.0006005768,0.0005540634,0.000201568,0.0001931445,0.0005045946,0.0006832653,0.0007460599,0.002188003],"category_scores_gemma":[0.0007378022,0.0003106931,0.0006669944,0.0002960593,0.0002733803,0.0003694313,0.000373336,0.0004891141,0.0003494263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009204075,"about_ca_system_score_gemma":0.00134166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004768268,"about_ca_topic_score_gemma":0.004528312,"domain_scores_codex":[0.9998661,0.00003505778,0.000004835743,0.00002198002,0.00005212268,0.00001984972],"domain_scores_gemma":[0.9998015,0.00009473821,0.00003293588,0.0000131526,0.00004031104,0.00001750803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005218449,0.00002130384,0.0003805519,0.00005762601,0.00001875618,0.00003793433,0.00002113318,0.9824604,0.004186481,0.001401494,0.000750982,0.0106112],"study_design_scores_gemma":[0.00001109675,0.00003127569,0.0001124926,0.000003171162,0.000004214422,0.00001984405,0.000002474929,0.9973094,0.00154741,0.0003096323,0.0006450757,0.000004016776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06301436,0.0004911669,0.9265568,0.0002116843,0.00002327594,0.00007950043,0.0002879695,0.001656336,0.007678862],"genre_scores_gemma":[0.6567958,0.000296835,0.3353235,0.0001616085,0.0000182187,0.0002325839,0.0005189573,0.0006045993,0.006047899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004768268,"threshold_uncertainty_score":0.009481072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009309147879850935,"score_gpt":0.2574633288249409,"score_spread":0.24815418094509,"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."}}