{"id":"W2594320623","doi":"10.1109/antem.2004.7860697","title":"Comparison of immersion liquids for tissue sensing adaptive radar","year":2004,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Immersion (mathematics); Radar; Computer science; Radar detection; Computer vision; Remote sensing; Biomedical engineering; Artificial intelligence; Electronic engineering; Engineering; Geology; Telecommunications; Mathematics","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":[0.001294499,0.0008640408,0.0005510582,0.0005237305,0.000232388,0.001048363,0.0006522472,0.0005739294,0.001808317],"category_scores_gemma":[0.004771354,0.0002956001,0.0003919917,0.0004934164,0.0004749348,0.001515461,0.0007784275,0.0004472347,0.0005538506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004420888,"about_ca_system_score_gemma":0.0003007723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061692,"about_ca_topic_score_gemma":0.001532603,"domain_scores_codex":[0.9990376,0.0002819472,0.00007336384,0.00009828306,0.0004033746,0.0001054056],"domain_scores_gemma":[0.996972,0.001731484,0.0002236806,0.0001215365,0.0008500882,0.0001012479],"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":[0.0008267522,0.00003690931,0.0004093429,0.0002962667,0.00002364233,0.00007081403,0.0001092615,0.001251774,0.9693145,0.0004665616,0.0001252761,0.02706885],"study_design_scores_gemma":[0.00002241859,0.001317042,0.0006536825,0.00002475258,0.00005542372,0.0001784112,0.0001195521,0.007573741,0.9864926,0.0001324277,0.003396275,0.00003373173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8408553,0.01940833,0.1303624,0.0006506635,0.0003822871,0.0002279166,0.0002610644,0.0007642268,0.007087797],"genre_scores_gemma":[0.9096731,0.01177314,0.07162809,0.0002598626,0.00008923573,0.0001342422,0.0004077598,0.0003648369,0.005669702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001808317,"threshold_uncertainty_score":0.006846011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106535219319813,"score_gpt":0.2828335117957323,"score_spread":0.2617681596025342,"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."}}