{"id":"W2120752975","doi":"10.1109/tmtt.2010.2052662","title":"Exploring Joint Tissues With Microwave Imaging","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Microwave imaging; Knee Joint; Biomedical engineering; Patellar tendon; Microwave; Dielectric; Materials science; Meniscus; Finite-difference time-domain method; Patellar ligament; Medical imaging; Tendon; Acoustics; Joint (building); Optics; Computer science; Physics; Engineering; Anatomy; Medicine; Optoelectronics; Structural engineering; Surgery; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004492884,0.0003364513,0.0002880985,0.0003576668,0.0002249933,0.0001224958,0.0001398101,0.00006972898,0.00005326591],"category_scores_gemma":[0.000002995885,0.0003026689,0.0001047433,0.0002030075,0.00022273,0.0003038985,0.000002433032,0.0006939743,0.00002236661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008342,"about_ca_system_score_gemma":0.00001004451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002108909,"about_ca_topic_score_gemma":0.00003659751,"domain_scores_codex":[0.9988823,0.00006248843,0.0002527614,0.0003476254,0.0001084871,0.0003463487],"domain_scores_gemma":[0.9993114,0.00008314476,0.00003613709,0.000408666,0.00004415114,0.0001164297],"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":[0.00002860395,0.0000343103,0.000007569873,0.00004250226,0.00007497019,0.00001269478,0.0004536233,0.00007124329,0.8730981,0.0003774242,0.0000785525,0.1257203],"study_design_scores_gemma":[0.0001378474,0.00003678522,0.00001055797,0.0001158241,0.00009096169,0.000217197,0.0001803233,0.0001293062,0.9955588,0.001341332,0.001783195,0.0003978597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4195961,0.0001059779,0.5754778,0.00006531278,0.0001738655,0.0001202722,0.0000117441,0.001107177,0.003341786],"genre_scores_gemma":[0.9856794,0.0003377918,0.01341015,0.0001000367,0.00006662973,0.00008607168,0.000002849929,0.00008606214,0.0002309378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5660834,"threshold_uncertainty_score":0.9999425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778880396271793,"score_gpt":0.214782111830632,"score_spread":0.1969933078679141,"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."}}