{"id":"W2059670388","doi":"10.1063/1.4797484","title":"Magnetic resonance imaging of microvessels using iron-oxide nanoparticles","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Magnetic resonance imaging; Magnetic particle imaging; Materials science; Scanner; Biomedical engineering; Image resolution; Visualization; Arteriole; Iron oxide; Nuclear magnetic resonance; Clinical imaging; Magnetic nanoparticles; Nanoparticle; Nanotechnology; Radiology; Optics; Computer science; Medicine; Microcirculation; Metallurgy; Physics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003460162,0.0001428092,0.0001307437,0.0002841135,0.0001285958,0.0001518044,0.0001430634,0.0002459808,0.0002984341],"category_scores_gemma":[0.0003284646,0.0001351163,0.00009042186,0.00008778147,0.0001478578,0.0002265767,0.0001548748,0.0001570851,0.00007954168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607959,"about_ca_system_score_gemma":0.0001478571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006555857,"about_ca_topic_score_gemma":0.0009693852,"domain_scores_codex":[0.9999367,0.00001893902,0.000002658943,0.00001846991,0.00001389081,0.000009309484],"domain_scores_gemma":[0.999897,0.00004819187,0.00001673106,0.000007145017,0.00001785482,0.00001302403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003956133,0.00001129562,0.0003119328,0.00002260556,0.000002154222,0.00003648097,0.00001374472,0.0001497981,0.9975166,0.00008239551,0.00002201556,0.001791391],"study_design_scores_gemma":[0.00002002455,0.0002722509,0.004676558,0.000008061659,0.00002044561,0.0004072374,0.00002925828,0.01339847,0.980109,0.0001388924,0.0009082095,0.00001154249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949019,0.00101711,0.04849238,0.0000945611,0.00001684782,0.00002677239,0.00003825923,0.0001772544,0.001117891],"genre_scores_gemma":[0.9396497,0.0005779897,0.05843796,0.00005737786,0.00001136997,0.00003041462,0.00004570367,0.0000221779,0.001167244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006555857,"threshold_uncertainty_score":0.001829922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389626251497365,"score_gpt":0.2798637996610144,"score_spread":0.2659675371460408,"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."}}