{"id":"W3197727415","doi":"10.1101/2021.08.31.458286","title":"The sensitivity of magnetic particle imaging and fluorine-19 magnetic resonance imaging for cell tracking","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Magnetic resonance imaging; In vivo; Magnetic particle imaging; Preclinical imaging; Nuclear magnetic resonance; Cell; Sensitivity (control systems); Tracking (education); Nuclear medicine; Chemistry; Materials science; Medicine; Magnetic nanoparticles; Nanotechnology; Radiology; Physics; Nanoparticle; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005774254,0.0003139013,0.0003180548,0.00006058156,0.0001937732,0.0003256649,0.0002075496,0.00008302181,0.00001204212],"category_scores_gemma":[0.0001764891,0.0003278276,0.0000849724,0.0002628502,0.0001565414,0.000121228,0.0002028606,0.0002438846,0.00000270995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005969462,"about_ca_system_score_gemma":0.00009817332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001636238,"about_ca_topic_score_gemma":0.000003771724,"domain_scores_codex":[0.9983519,0.00008137452,0.0004884702,0.0004772097,0.0001856076,0.0004154255],"domain_scores_gemma":[0.9983658,0.0002999342,0.0001342874,0.0007494809,0.0002997805,0.0001507141],"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":[0.00001104821,0.00004299945,0.005851695,0.0003689788,0.000006065794,0.000008483263,0.00002755898,0.0002435468,0.992605,0.0001718965,0.00006835583,0.0005944123],"study_design_scores_gemma":[0.0005708229,0.00001835705,0.1083341,0.0001827987,0.00008387746,1.066664e-7,0.00003063676,0.123628,0.761711,0.000004202206,0.004971918,0.0004642107],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961089,0.0317026,0.005433476,0.0004116771,0.0003303214,0.0006645701,0.0001043517,0.0002591829,0.000004837173],"genre_scores_gemma":[0.9943582,0.001169351,0.003989322,0.00005694924,0.000118843,0.0002129761,4.352157e-7,0.0000888577,0.000005016481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2308939,"threshold_uncertainty_score":0.9999174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007345480395153178,"score_gpt":0.1943105151009188,"score_spread":0.1869650347057656,"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."}}