{"id":"W1574302281","doi":"10.1002/mrm.24134","title":"Tuning and amplification strategies for intravascular imaging coils","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research","keywords":"Amplifier; SIGNAL (programming language); Electromagnetic coil; Noise (video); Computer science; Signal-to-noise ratio (imaging); Electronic engineering; Acoustics; Artificial intelligence; Electrical engineering; Physics; Telecommunications; Engineering; Bandwidth (computing); Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008006587,0.0007025298,0.000327588,0.0004274776,0.0002448406,0.0005351831,0.0007002502,0.0007156013,0.001079427],"category_scores_gemma":[0.002621703,0.0003841309,0.0001871473,0.0003062077,0.0005934816,0.0008070082,0.0004278182,0.0003582894,0.0007242523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003325152,"about_ca_system_score_gemma":0.0001333694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000109067,"about_ca_topic_score_gemma":0.0002487376,"domain_scores_codex":[0.9990957,0.0002569105,0.00006464224,0.0002313579,0.0002888741,0.00006261477],"domain_scores_gemma":[0.9986044,0.000676901,0.0003195283,0.0001077044,0.0002478591,0.00004369541],"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":[0.0000856133,0.0000223514,0.0002579837,0.0001089533,0.00000810311,0.00008942392,0.00009920648,0.0009764542,0.9720495,0.001294963,0.0002058303,0.02480163],"study_design_scores_gemma":[0.00002691467,0.0004622716,0.001083544,0.00002614457,0.0000456006,0.0007962277,0.00005246985,0.01395458,0.9723992,0.001101009,0.01001211,0.0000399579],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2119118,0.003861821,0.7747217,0.0006646488,0.000131852,0.0002965628,0.00003960016,0.0006854054,0.007686604],"genre_scores_gemma":[0.7046902,0.001092461,0.2903619,0.0004175089,0.0001364278,0.0001899679,0.00004315077,0.00009453404,0.002973823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001079427,"threshold_uncertainty_score":0.004234314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04282319499147784,"score_gpt":0.3220657199680052,"score_spread":0.2792425249765274,"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."}}