{"id":"W4410882353","doi":"10.1016/j.ohx.2025.e00659","title":"Printed, dual-loop magnetic field sniffer probe for bench measurements on radio frequency MRI coils","year":2025,"lang":"en","type":"article","venue":"HardwareX","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nuclear magnetic resonance; Magnetic field; Dual loop; Loop (graph theory); Dual (grammatical number); Radio frequency; Physics; Acoustics; Biomedical engineering; Electrical engineering; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001103091,0.0001585131,0.0002314215,0.00009407075,0.00009887383,0.00001474319,0.0001069027,0.000105673,0.0002572027],"category_scores_gemma":[0.0001530607,0.0001380373,0.0001026597,0.0001772586,0.00003006809,0.00002987385,0.00003079804,0.0001719252,0.00003136165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009576727,"about_ca_system_score_gemma":0.00008556987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001730517,"about_ca_topic_score_gemma":0.000007214671,"domain_scores_codex":[0.9989696,0.00001161263,0.0002516237,0.0003366716,0.0001819849,0.0002485439],"domain_scores_gemma":[0.9991474,0.00007492562,0.0000520258,0.0004757487,0.0001682713,0.00008164907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007616602,0.002112889,0.0167166,0.001057732,0.0002010946,0.00004254574,0.0003005695,0.00004095173,0.3135669,0.1155924,0.3870189,0.1625878],"study_design_scores_gemma":[0.0057125,0.003544011,0.0134039,0.0016394,0.0004862622,0.00004238054,0.00009923384,0.0005952752,0.5447631,0.04274122,0.3862173,0.0007554172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007794349,0.0007734892,0.8868723,0.01726499,0.0003041971,0.005859804,0.00004694229,0.0005849988,0.08049892],"genre_scores_gemma":[0.6683482,0.0003868776,0.2576856,0.01165764,0.0003848216,0.005148386,0.0002061355,0.00009897462,0.05608336],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6605538,"threshold_uncertainty_score":0.5628996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349982215133771,"score_gpt":0.3350022558750652,"score_spread":0.3000040343616881,"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."}}