{"id":"W2950243281","doi":"10.1109/tbme.2019.2922879","title":"Wireless Resonant Circuits Printed Using Aerosol Jet Deposition for MRI Catheter Tracking","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Quest University Canada","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health","keywords":"Catheter; Tracking (education); Electromagnetic coil; Materials science; Inductor; Jet (fluid); Magnetic resonance imaging; Biomedical engineering; Deposition (geology); Computer science; Electrical engineering; Radiology; Engineering; Medicine; Aerospace 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008677402,0.0001770017,0.0002435834,0.0001752674,0.00008472068,0.00001415189,0.00007538281,0.0001641595,0.0000324959],"category_scores_gemma":[0.000004871289,0.0001606518,0.0001582317,0.0002664199,0.00003365659,0.00008270097,0.000001188631,0.0002847452,0.00001169283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607151,"about_ca_system_score_gemma":0.00003880089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009509836,"about_ca_topic_score_gemma":0.000001029194,"domain_scores_codex":[0.9989042,0.000005477062,0.0002781194,0.0003034602,0.0002132501,0.0002954727],"domain_scores_gemma":[0.9993841,0.0000787639,0.00004357766,0.0002573812,0.00006286149,0.000173316],"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":[0.0000478802,0.0003724013,0.000006069005,0.0001429556,0.00003555002,0.000005638515,0.00007980823,0.01100941,0.9272458,0.00009753805,0.00001497583,0.06094201],"study_design_scores_gemma":[0.001074228,0.000365201,0.0000285588,0.0005569358,0.00008951168,0.0001142163,0.00002573491,0.52096,0.4744177,0.00001042274,0.002118474,0.000239091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1162791,0.0000179602,0.8820346,0.0002564016,0.0002278508,0.0008360402,0.00003596641,0.0002959458,0.00001612853],"genre_scores_gemma":[0.9492507,0.00003732405,0.05018225,0.0001054437,0.00008850713,0.0001936783,0.0000210521,0.00005542476,0.00006564651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8329716,"threshold_uncertainty_score":0.6551191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015013884190292,"score_gpt":0.2844496083779486,"score_spread":0.2642994695360457,"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."}}