{"id":"W2047593411","doi":"10.1109/aps.2014.6904666","title":"Sensitivity-based quantitative imaging using planar raster scanning","year":2014,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Raster graphics; Calibration; Planar; Sensitivity (control systems); Raster scan; Computer science; Computer vision; Raster data; Artificial intelligence; Electronic engineering; Mathematics; Computer graphics (images); Engineering","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":[0.0005706444,0.0005892998,0.0003476605,0.0007384244,0.0001110804,0.0006217518,0.0005214424,0.0003884281,0.001174438],"category_scores_gemma":[0.001427138,0.0002733602,0.0002798756,0.0007079239,0.0005402474,0.0006771071,0.000668221,0.0003697911,0.0002716822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004913269,"about_ca_system_score_gemma":0.0003729751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004122912,"about_ca_topic_score_gemma":0.0003496062,"domain_scores_codex":[0.9994296,0.0001254677,0.00001868889,0.00009536886,0.0003029559,0.00002780237],"domain_scores_gemma":[0.9992548,0.0003471908,0.0001180728,0.000106534,0.0001549297,0.00001841492],"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":[0.0001019647,0.00003815292,0.0007124523,0.0001914592,0.00003274915,0.0001009063,0.00009588388,0.02857515,0.885039,0.008374822,0.0004582861,0.07627931],"study_design_scores_gemma":[0.00002021684,0.0002337461,0.001924029,0.00002384983,0.00003495629,0.0008831552,0.00004597259,0.5239721,0.4639789,0.005189131,0.003619157,0.00007478886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0251856,0.000146099,0.972005,0.0000758099,0.00001280158,0.00004808341,0.00007777632,0.0007897834,0.001659095],"genre_scores_gemma":[0.4116727,0.0003556545,0.5863912,0.00007823526,0.00002024657,0.0001275848,0.0001371229,0.0001215755,0.001095663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001174438,"threshold_uncertainty_score":0.0039289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01485848517194057,"score_gpt":0.2384006547408331,"score_spread":0.2235421695688925,"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."}}