{"id":"W2146699192","doi":"10.1002/mrm.24845","title":"Prospective motion correction using inductively coupled wireless RF coils","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Association Canadienne des Technologues en Radiation Médicale; National Institute of Biomedical Imaging and Bioengineering; Lucas Foundation","keywords":"Wireless; Electromagnetic coil; Computer science; Match moving; Tracking (education); Radio frequency; Transmitter; Orientation (vector space); Scanner; Head (geology); SIGNAL (programming language); Position (finance); Computer vision; Artificial intelligence; Acoustics; Motion (physics); Physics; Telecommunications; Channel (broadcasting); Electrical engineering; Engineering; Mathematics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001871448,0.00018641,0.0003930086,0.0001727362,0.000077103,0.000008291693,0.00008420343,0.0001207849,0.0004717608],"category_scores_gemma":[0.0001657063,0.0001535366,0.00003163659,0.0006697732,0.0002745069,0.0001325864,0.00002776898,0.0003520733,0.00002550001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002758317,"about_ca_system_score_gemma":0.00004937735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001305969,"about_ca_topic_score_gemma":0.00003054657,"domain_scores_codex":[0.9985836,0.0000252244,0.0004033003,0.0003971707,0.0003024251,0.0002882481],"domain_scores_gemma":[0.9991377,0.00006613274,0.0001200948,0.0003131618,0.0002566677,0.0001062226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002423781,0.0005678679,0.0938582,0.00009126354,0.000006908857,0.00004286199,0.001264384,0.0001660786,0.2298653,0.001581586,0.004477827,0.6678354],"study_design_scores_gemma":[0.003361188,0.001195651,0.8545054,0.00109101,0.00006103046,0.0001986307,0.001057136,0.1235688,0.005300155,0.005116674,0.004238921,0.0003053753],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451998,0.002085253,0.04413659,0.002235929,0.0002660097,0.002573432,0.000001709763,0.0001679395,0.003333308],"genre_scores_gemma":[0.9868132,0.0004514935,0.009857015,0.0003657643,0.0002958902,0.0005593816,0.00001278311,0.00003260815,0.001611848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7606472,"threshold_uncertainty_score":0.6261042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248815115161684,"score_gpt":0.3143204948717577,"score_spread":0.2894389833555893,"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."}}