{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008028903,0.0008268752,0.000435363,0.0003758665,0.0002584212,0.0003917812,0.0003366988,0.0005248206,0.0005045906],"category_scores_gemma":[0.0009033625,0.0003058005,0.0002079783,0.0002875501,0.0003671276,0.0004046766,0.0002385881,0.000406879,0.000351835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005764914,"about_ca_system_score_gemma":0.0005830264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008914711,"about_ca_topic_score_gemma":0.002513817,"domain_scores_codex":[0.9996616,0.00006751288,0.00003744571,0.00008428081,0.0001080938,0.00004117167],"domain_scores_gemma":[0.9996669,0.000106077,0.00007099645,0.00003338557,0.00009285619,0.00002977674],"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":[0.00001401335,0.00001030752,0.00002528811,0.00002441649,0.000001425027,0.00000969829,0.000007599494,0.0001777508,0.9989088,0.00007947756,0.00001137913,0.0007298542],"study_design_scores_gemma":[0.000003567157,0.00004700624,0.00009485067,0.00000212222,0.000003161032,0.00001859376,0.000002993795,0.0007781249,0.9982294,0.00001958827,0.0007976437,0.000002941383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8574969,0.001822317,0.1360633,0.0001991355,0.00008630411,0.0007405229,0.0004676293,0.0005981626,0.002525844],"genre_scores_gemma":[0.7187658,0.001836858,0.2744645,0.000101405,0.00002838822,0.0005846132,0.0009075995,0.000276408,0.003034507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008914711,"threshold_uncertainty_score":0.004246116,"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."}}