{"id":"W2905337440","doi":"10.1109/antem.2018.8572994","title":"A Dynamically Balanced OpenMP-CUDA Implementation of PDE-Based Contrast Source Inversion for Microwave Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"CUDA; Computer science; Parallel computing; Speedup; Solver; Acceleration; Computational science; Inversion (geology); General-purpose computing on graphics processing units; Algorithm; Graphics; Computer graphics (images); Physics; Geology","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":[0.0001701028,0.0001403822,0.0002068716,0.0001303225,0.00006461412,0.00004194173,0.0001381349,0.00002809144,0.0001676988],"category_scores_gemma":[0.00001186521,0.0001402981,0.0001094314,0.0001442259,0.00006514347,0.00008105137,0.00002069592,0.0000511272,0.00001688688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006758227,"about_ca_system_score_gemma":0.00002114408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002398566,"about_ca_topic_score_gemma":0.0001851647,"domain_scores_codex":[0.9991576,0.00001686788,0.00028868,0.000186406,0.00009584685,0.0002546063],"domain_scores_gemma":[0.9995244,0.00005168841,0.00005616556,0.0001941449,0.000117036,0.00005657529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000218686,0.000009915474,0.006689972,0.00009786888,0.00006725391,4.151765e-7,0.0002075146,0.00122548,0.9575033,0.00001618665,0.005013085,0.02914721],"study_design_scores_gemma":[0.000807861,0.00002955567,0.000990745,0.00003582005,0.00005396585,0.000001383605,0.0003235427,0.5316485,0.4650488,0.00002941158,0.0008639203,0.000166495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3911691,0.00001951315,0.607744,0.0002127474,0.00007392484,0.0001376714,0.00002084025,0.0001222844,0.0004999595],"genre_scores_gemma":[0.9837241,0.000002338703,0.01574893,0.0002643967,0.00005456286,0.00001364721,0.00006701263,0.0000308138,0.00009419894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.592555,"threshold_uncertainty_score":0.572119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005421309118459037,"score_gpt":0.2386564594136545,"score_spread":0.2332351502951955,"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."}}