{"id":"W1480624346","doi":"10.1002/mrm.24883","title":"Design of k‐space channel combination kernels and integration with parallel imaging","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"Computer science; Channel (broadcasting); Pipeline (software); Computation; Calibration; k-space; Image quality; Algorithm; Arc (geometry); Image (mathematics); Artificial intelligence; Computer vision; Mathematics; Telecommunications; Geometry; Fourier transform","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":[0.0003243137,0.0001186599,0.0002830608,0.0001315935,0.00002328255,0.000006471521,0.0000688543,0.00003259917,0.0001484515],"category_scores_gemma":[0.0001676411,0.00007930182,0.000008535458,0.0002754821,0.0003613605,0.0000704017,0.00001916253,0.0001701762,0.000003519351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002453271,"about_ca_system_score_gemma":0.00002800771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005379045,"about_ca_topic_score_gemma":0.000004182699,"domain_scores_codex":[0.9990268,0.00004072741,0.0002937199,0.0002142927,0.0002633759,0.0001611005],"domain_scores_gemma":[0.9993404,0.0001215773,0.00008594359,0.0002201827,0.0001409034,0.00009106186],"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":[0.0006367148,0.001096427,0.05709432,0.0005799989,0.00001659883,0.0000829639,0.004070853,0.00004669311,0.1351454,0.01183443,0.05010021,0.7392954],"study_design_scores_gemma":[0.01152209,0.005672411,0.4972926,0.005539632,0.0001275959,0.0002705897,0.001358006,0.4495965,0.004131744,0.02029617,0.003761342,0.0004313132],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.279496,0.01225312,0.5644794,0.1352629,0.00006655462,0.004656383,0.00000155556,0.0001822695,0.00360179],"genre_scores_gemma":[0.9478112,0.0009418511,0.04974626,0.0006030136,0.00003542944,0.0003058267,0.000008783392,0.00001470008,0.000532894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7388641,"threshold_uncertainty_score":0.3233835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129562284343092,"score_gpt":0.2847695058566109,"score_spread":0.26347388301318,"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."}}