{"id":"W4386019240","doi":"10.1002/mrm.29781","title":"A correction algorithm for improved magnetic field monitoring with distal field probes","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canada Research Chairs; Canada Foundation for Innovation","keywords":"Imaging phantom; Algorithm; Residual; Field (mathematics); Robustness (evolution); Isocenter; Computer science; Artifact (error); Phase (matter); Image quality; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Optics; Physics","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":[0.000898517,0.001055618,0.0005465875,0.000791951,0.0004434446,0.0007376299,0.001124996,0.001156094,0.001965054],"category_scores_gemma":[0.003889528,0.0003978069,0.0005367164,0.0008591494,0.0003891209,0.000896267,0.0006487957,0.001084161,0.0009831145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004640624,"about_ca_system_score_gemma":0.001251617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266001,"about_ca_topic_score_gemma":0.002385966,"domain_scores_codex":[0.999442,0.00009551412,0.00004533316,0.0001471036,0.0002317113,0.0000382777],"domain_scores_gemma":[0.9988366,0.0003451002,0.0001529075,0.0001452824,0.0004697659,0.00005046327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004758333,0.00009911237,0.001627718,0.0001182852,0.00006153839,0.0001576575,0.0001733357,0.05161336,0.1542763,0.004702502,0.002671716,0.7840226],"study_design_scores_gemma":[0.00007099307,0.0002589189,0.001573938,0.00002127388,0.00005863738,0.0005735774,0.00002293043,0.9007,0.08391798,0.001873722,0.01086724,0.00006084811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008219518,0.00007713795,0.9904332,0.00006082075,0.00002817457,0.00003260577,0.00001273035,0.0009489079,0.0001868424],"genre_scores_gemma":[0.04964346,0.00007441628,0.9487106,0.00005075669,0.00002171047,0.00006681131,0.0000814081,0.0001802116,0.001170623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002266001,"threshold_uncertainty_score":0.006573737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540256450890872,"score_gpt":0.3171905555382914,"score_spread":0.3017879910293827,"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."}}