{"id":"W2000937513","doi":"10.1088/0031-9155/48/17/301","title":"Space–time relationship in continuously moving table method for large FOV peripheral contrast-enhanced magnetic resonance angiography","year":2003,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Fondation pour la Recherche Médicale; Heart and Stroke Foundation of Canada","keywords":"Sampling (signal processing); Contrast (vision); Peripheral; Computer science; Computer vision; Image quality; Artificial intelligence; Image (mathematics)","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":[0.0004829466,0.0002088315,0.0001571582,0.0002669898,0.0001287666,0.000383875,0.0003945532,0.0003770248,0.001716434],"category_scores_gemma":[0.001601036,0.0001711998,0.0001816365,0.0004055306,0.0001850478,0.0003922831,0.0002021026,0.0002603547,0.000321092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653499,"about_ca_system_score_gemma":0.0003145057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608986,"about_ca_topic_score_gemma":0.001407208,"domain_scores_codex":[0.9998507,0.00005739677,0.0000055492,0.00002238572,0.00005418009,0.000009563792],"domain_scores_gemma":[0.9995474,0.0003051002,0.00004618103,0.0000310565,0.00005409098,0.00001609793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008499351,0.0001101057,0.00469477,0.0002785296,0.00006032005,0.0008978366,0.000301481,0.551935,0.1838574,0.03447951,0.001838213,0.2206969],"study_design_scores_gemma":[0.00001429861,0.00008707196,0.001342266,0.00000448089,0.000008970729,0.0002877034,0.00001149891,0.9840425,0.01047378,0.001602071,0.002108485,0.00001686947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07376467,0.0003209628,0.9238717,0.00005666562,0.00001588982,0.00003332888,0.00004763449,0.0004885598,0.001400601],"genre_scores_gemma":[0.5773497,0.0002748438,0.4202983,0.00002692535,0.00001417979,0.00007068899,0.00009534549,0.0001319129,0.00173807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001716434,"threshold_uncertainty_score":0.005742073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07100513317592093,"score_gpt":0.3934115188011496,"score_spread":0.3224063856252286,"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."}}