{"id":"W2069339299","doi":"10.1063/1.3202410","title":"The integration of real and virtual magnetic resonance imaging experiments in a single instrument","year":2009,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"Western Economic Diversification Canada","keywords":"Magnetic resonance imaging; Computer science; System integration; Virtual instrument; Magnetic field; Nuclear magnetic resonance; Simulation; Data acquisition; Physics; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757285,0.00008285889,0.0002044157,0.00006788101,0.00007771445,0.000014596,0.00009765753,0.00001696123,0.00001064738],"category_scores_gemma":[0.0000587109,0.00005771787,0.00003685397,0.0003415242,0.0002169609,0.00009141155,0.00003528603,0.00006372394,9.569414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006163736,"about_ca_system_score_gemma":0.00004195264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001047853,"about_ca_topic_score_gemma":0.000002403011,"domain_scores_codex":[0.9989507,0.00002217219,0.0004489354,0.0001982441,0.0002490816,0.0001309014],"domain_scores_gemma":[0.9994029,0.00001266792,0.0001626446,0.0002940084,0.00008130701,0.00004646991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001284782,0.0001187897,0.0005873772,0.00008754603,5.0883e-7,3.6662e-7,0.0000662454,3.04526e-8,0.07576992,0.002337311,0.00008937876,0.9209297],"study_design_scores_gemma":[0.004050096,0.002408903,0.1451143,0.06413159,0.0001415552,0.00008832182,0.001272448,0.00201179,0.6889413,0.005663696,0.08559145,0.0005845041],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720257,0.02351932,0.0001014751,0.0008754228,0.00008208373,0.001057994,0.000007873311,0.00001675484,0.002313409],"genre_scores_gemma":[0.9691566,0.02498766,0.005425358,0.0001396141,0.000006188807,0.0000344958,0.00001229123,0.000004411807,0.0002333329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9203452,"threshold_uncertainty_score":0.2353666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744156516512115,"score_gpt":0.3337736939155023,"score_spread":0.3063321287503812,"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."}}