{"id":"W2076778619","doi":"10.1118/1.2241635","title":"TU‐E‐ValB‐07: A Segmentation and Leaf Sequencing Algorithm for IMAT","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Tomotherapy; Algorithm; Intensity modulation; Histogram; Radiation treatment planning; Imaging phantom; Computer science; Intensity (physics); Arc (geometry); Nuclear medicine; Dosimetry; Segmentation; Monte Carlo method; Mathematics; Medicine; Computer vision; Radiation therapy; Physics; Statistics; Optics; Surgery; Geometry; Image (mathematics)","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":[0.0008269306,0.0007371219,0.0005727974,0.001254283,0.0004036316,0.001051862,0.001215253,0.0009270951,0.004886595],"category_scores_gemma":[0.001536953,0.0005830736,0.000678408,0.001088914,0.0003665202,0.0006412657,0.000640988,0.001004351,0.002744942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009570532,"about_ca_system_score_gemma":0.001015421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005166669,"about_ca_topic_score_gemma":0.00638022,"domain_scores_codex":[0.9995395,0.00009628035,0.0000334139,0.00007658123,0.0002228459,0.00003145304],"domain_scores_gemma":[0.9996806,0.00008887018,0.00003528774,0.00003737985,0.0001359255,0.00002185885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003408996,0.00009158124,0.001301056,0.0001634468,0.0000873078,0.00008512037,0.0001754913,0.1052642,0.07120952,0.00550339,0.01039831,0.8053796],"study_design_scores_gemma":[0.00006267908,0.00007419123,0.001176782,0.00002372473,0.00001655062,0.0001509239,0.0000198634,0.9468946,0.03103834,0.002769156,0.01774023,0.000032958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00491964,0.0001319335,0.9884691,0.00004119125,0.00001868652,0.00005045602,0.00008318235,0.005020647,0.001265228],"genre_scores_gemma":[0.03241729,0.00007431523,0.9634522,0.00008667501,0.00001361255,0.0001504046,0.0004440984,0.001319216,0.002042126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005166669,"threshold_uncertainty_score":0.01634729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009934232743538,"score_gpt":0.2876261297699828,"score_spread":0.2775267874425474,"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."}}