{"id":"W2744257170","doi":"10.1016/j.cmpb.2017.08.001","title":"Massively parallel simulator of optical coherence tomography of inhomogeneous turbid media","year":2017,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Nvidia","keywords":"Monte Carlo method; Computer science; CUDA; Massively parallel; Computational science; Optical coherence tomography; Computation; Photon; Coherence (philosophical gambling strategy); Optics; Simulation; Computer graphics (images); Parallel computing; Algorithm; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005446315,0.0001860287,0.000491167,0.0002211355,0.00004493455,0.00002818624,0.0004263489,0.0001369902,0.0000104439],"category_scores_gemma":[0.00006071365,0.0001557948,0.00007825263,0.0003666714,0.0009448854,0.00006663649,0.0001276093,0.0001845661,5.591745e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007343498,"about_ca_system_score_gemma":0.00001227657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002366784,"about_ca_topic_score_gemma":0.000006381531,"domain_scores_codex":[0.9987784,0.00005378658,0.0004887934,0.0002471168,0.0001781009,0.0002537911],"domain_scores_gemma":[0.9987366,0.0003390353,0.0001264539,0.0005479667,0.00009028134,0.0001596634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002071116,0.0001262648,0.02539973,0.0002595329,0.00007155058,0.000009015903,0.0003368864,0.0001907509,0.005554641,0.00184495,0.00001796673,0.966168],"study_design_scores_gemma":[0.004713546,0.001994333,0.7032615,0.001362551,0.0002434786,0.00005103527,0.0001900698,0.2409053,0.01746522,0.0250794,0.003544102,0.001189444],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4704627,0.002251458,0.5253333,0.0001602371,0.0004039233,0.0006893163,0.00001527499,0.0001125105,0.0005712686],"genre_scores_gemma":[0.5582956,0.0000758536,0.4415274,0.00000643944,0.0000509044,0.00002784207,0.000005129855,0.00001002657,8.377361e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9649786,"threshold_uncertainty_score":0.6353128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04749906059532106,"score_gpt":0.3319029568507994,"score_spread":0.2844038962554784,"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."}}