{"id":"W2031160510","doi":"10.1118/1.3476106","title":"Poster — Thur Eve — 01: Dynamic Aperture Optimization in MERT Using Direct Aperture Optimization","year":2010,"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":"McGill University","funders":"","keywords":"Collimator; Tomotherapy; Computer science; Aperture (computer memory); Dosimetry; Nuclear medicine; Radiation treatment planning; Medical physics; Optics; Physics; Radiation therapy; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005155622,0.0003863821,0.0002812031,0.0002257301,0.0002367206,0.0007009826,0.0004236824,0.0003944433,0.00677575],"category_scores_gemma":[0.0007784034,0.0002530205,0.0005451285,0.0002305406,0.0002200289,0.0003691834,0.0007839197,0.0004988841,0.0009611117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004478654,"about_ca_system_score_gemma":0.0005973282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001275215,"about_ca_topic_score_gemma":0.001864764,"domain_scores_codex":[0.9997895,0.00005131473,0.000008137749,0.00003741909,0.00009725583,0.00001642406],"domain_scores_gemma":[0.9997893,0.00008028209,0.00001852912,0.00003314612,0.00005922199,0.00001960577],"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":[0.0003972321,0.0001700235,0.00216898,0.0002344908,0.00007308701,0.0001900606,0.0001346049,0.7110328,0.0566549,0.009797851,0.01304349,0.2061025],"study_design_scores_gemma":[0.00007382785,0.0002062746,0.001692297,0.00002087875,0.00001902684,0.0002062967,0.00002365617,0.9415813,0.03194061,0.001959503,0.02223894,0.00003738093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364533,0.001014329,0.7927881,0.000681248,0.0003886647,0.0002687731,0.0005473613,0.003578622,0.06427961],"genre_scores_gemma":[0.5371345,0.0003292026,0.4280673,0.0002132214,0.00008418321,0.0001438834,0.0009005622,0.001775218,0.031352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00677575,"threshold_uncertainty_score":0.02266711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005945125637109013,"score_gpt":0.2695454857804894,"score_spread":0.2636003601433803,"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."}}