{"id":"W1972475251","doi":"10.1118/1.4757582","title":"2D lag and signal nonlinearity correction in an amorphous silicon EPID and their impact on pretreatment dosimetric verification","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Ontario Institute for Cancer Research","funders":"","keywords":"Multileaf collimator; SIGNAL (programming language); Image-guided radiation therapy; Linear particle accelerator; Nonlinear system; Detector; Dosimetry; Signal processing; Optics; Nuclear medicine; Medical imaging; Computer science; Physics; Beam (structure); Artificial intelligence; Digital signal processing; Medicine","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":[0.001227045,0.0004862028,0.0002500937,0.000605668,0.0001905975,0.0008381351,0.0004166428,0.0003983786,0.001292262],"category_scores_gemma":[0.005943402,0.0003103737,0.0002338857,0.0005836886,0.000271294,0.000801112,0.0007164962,0.0004694974,0.0002910258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005857459,"about_ca_system_score_gemma":0.000648513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007495636,"about_ca_topic_score_gemma":0.001160553,"domain_scores_codex":[0.9993327,0.0001482685,0.00004749005,0.0001157518,0.0003195306,0.00003614603],"domain_scores_gemma":[0.9976665,0.001259021,0.0004015062,0.0002494103,0.0003780138,0.00004560902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002316082,0.0001750478,0.04613987,0.000564446,0.00009500242,0.0003728142,0.0005367888,0.03941755,0.6439083,0.00211513,0.00070119,0.2636577],"study_design_scores_gemma":[0.0000759722,0.001557359,0.06940863,0.00007791488,0.0001556672,0.002002231,0.0001508241,0.2349656,0.6849478,0.000810881,0.005733302,0.0001138537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5723856,0.0008819991,0.4229489,0.0002163379,0.00007073621,0.0001268829,0.0001787512,0.001678042,0.00151278],"genre_scores_gemma":[0.8245747,0.0002897389,0.1732673,0.00009560635,0.00001785619,0.00005595176,0.0002049986,0.0002931656,0.001200585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001292262,"threshold_uncertainty_score":0.006489336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01326976548522462,"score_gpt":0.3023167883831816,"score_spread":0.289047022897957,"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."}}