{"id":"W2088159177","doi":"10.1002/mrm.21244","title":"Modeling pulsed magnetization transfer","year":2007,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Magnetization transfer; Experimental data; Magnetization; Relaxation (psychology); Representation (politics); Biological system; Nuclear magnetic resonance; Physics; Algorithm; Computational physics; Materials science; Statistical physics; Computer science; Mathematics; Statistics; Magnetic resonance imaging; Magnetic field","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":[0.0004861088,0.0001514841,0.0002958343,0.0002045795,0.00004170762,0.000003180639,0.00009412779,0.0001041341,0.0004394131],"category_scores_gemma":[0.00009629344,0.0001263223,0.00003344885,0.0006049295,0.0001168909,0.00004470445,0.00001231355,0.0002454877,0.00001144465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006872697,"about_ca_system_score_gemma":0.00002744173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001157269,"about_ca_topic_score_gemma":0.00008735827,"domain_scores_codex":[0.9985427,0.0000120478,0.0004976207,0.0003074136,0.0003041829,0.0003360028],"domain_scores_gemma":[0.9993886,0.00005055002,0.00001686639,0.0003285492,0.00009194963,0.0001234878],"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":[0.0004398646,0.000250317,0.006411224,0.00008769496,0.000001531504,0.0001516598,0.0007018336,0.001429513,0.03212555,0.006073497,0.0006737644,0.9516535],"study_design_scores_gemma":[0.01587185,0.004405458,0.1507884,0.002670927,0.000189449,0.0003858139,0.001516892,0.4848963,0.008223291,0.01596424,0.3140069,0.001080468],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1731015,0.01082031,0.7907325,0.004625127,0.00007531722,0.001044565,0.000002013085,0.0001806141,0.01941812],"genre_scores_gemma":[0.9725534,0.001678016,0.0222739,0.001308548,0.0002576035,0.00007727383,0.00002767335,0.00003333592,0.001790225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9505731,"threshold_uncertainty_score":0.5151272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458487046759706,"score_gpt":0.3262387515866379,"score_spread":0.3016538811190408,"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."}}