{"id":"W2791064555","doi":"10.3390/nano8040180","title":"Optimization of Iron Oxide Tracer Synthesis for Magnetic Particle Imaging","year":2018,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Magnetic particle imaging; Iron oxide nanoparticles; Materials science; Nanoparticle; Particle (ecology); Iron oxide; Magnetic nanoparticles; Magnetic particle inspection; Ethylene glycol; Nanotechnology; Chemistry; Organic chemistry","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":[0.0001217587,0.00008357564,0.0001333495,0.00003344958,0.00004112708,0.00003570291,0.00008587936,0.00002883963,0.0007038501],"category_scores_gemma":[0.00006046848,0.00008547562,0.0000273613,0.00008851709,0.00005388147,0.00009214471,0.00001123867,0.000007600173,0.00002921587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001239054,"about_ca_system_score_gemma":0.00000665902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002659855,"about_ca_topic_score_gemma":7.006416e-7,"domain_scores_codex":[0.999387,0.00001448512,0.0002858254,0.0001063196,0.00006126639,0.000145152],"domain_scores_gemma":[0.999623,0.000060096,0.00004596682,0.0001680332,0.00006655893,0.00003632256],"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":[0.00001525759,0.00002132617,0.0001366237,0.00004829406,0.000002914029,7.451357e-8,0.00005629344,0.00230466,0.9905701,0.0002231218,0.0001976148,0.006423748],"study_design_scores_gemma":[0.0002161899,0.00002401575,0.001217584,0.00001463117,0.00002213045,0.000001512287,0.00001795072,0.06298552,0.9340671,0.00004653393,0.001291641,0.00009513773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829442,0.00007773848,0.01597505,0.0001020494,0.0001591384,0.0002821599,0.0000440943,0.0001593,0.0002562819],"genre_scores_gemma":[0.9868164,0.00002198741,0.0128101,0.00002150237,0.00009295853,0.0001514796,0.000008271695,0.00002543124,0.00005184786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06068086,"threshold_uncertainty_score":0.7706665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008213240121817466,"score_gpt":0.2163403693523267,"score_spread":0.2081271292305092,"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."}}