{"id":"W4252333872","doi":"10.15680/ijirset.2015.0407201","title":"Effects of Coating and Stirring on Superparamagnetic Iron Oxide Nanoparticles Size and Magnetic Characteristics","year":2015,"lang":"en","type":"article","venue":"International Journal of Innovative Research in Science Engineering and Technology","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Superparamagnetism; Coating; Nanoparticle; Materials science; Magnetic nanoparticles; Iron oxide; Chemical engineering; Oxide; Iron oxide nanoparticles; Nanotechnology; Metallurgy; Magnetization; Magnetic field; Physics; Engineering","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":[0.0009637527,0.00007234965,0.0001338494,0.0009211033,0.00002427382,0.00005102191,0.0002016862,0.00003796348,8.997035e-7],"category_scores_gemma":[0.002025169,0.00006794667,0.000004382693,0.00105181,0.0004583308,0.0001433152,0.0001028548,0.0002433548,3.56967e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005269329,"about_ca_system_score_gemma":0.00004506603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002674059,"about_ca_topic_score_gemma":3.341927e-7,"domain_scores_codex":[0.9990714,0.00001724421,0.0002943879,0.0001047586,0.0003419445,0.0001702728],"domain_scores_gemma":[0.9987796,0.0004151273,0.00005067504,0.00006045942,0.000622704,0.00007141336],"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":[0.00001561584,0.00002688652,0.005784116,0.00003937072,0.000004180813,0.00001483401,0.0001906383,0.0003657692,0.9660936,0.004969546,0.000001939552,0.02249352],"study_design_scores_gemma":[0.001520019,0.0009020347,0.1960068,0.0006454642,0.000003491456,0.0001653994,0.0007382721,0.0268603,0.7714767,0.001313207,0.0001800876,0.000188225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987427,0.0004452327,0.0001643868,0.0004497009,0.00007391825,0.00007820014,0.000001459138,0.0000177598,0.00002666428],"genre_scores_gemma":[0.997559,0.0002290656,0.002173395,0.000005221295,0.00001723536,0.000006897737,1.445813e-7,0.000006635654,0.000002431821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1946169,"threshold_uncertainty_score":0.2770785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864384314072387,"score_gpt":0.3023506767649219,"score_spread":0.283706833624198,"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."}}