{"id":"W2899322485","doi":"10.1063/1.5052646","title":"A mechanically driven magnetic particle imaging scanner","year":2018,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Magnetic particle imaging; Excitation; Physics; Magnetic field; Harmonics; Decoupling (probability); Magnet; Optics; Magnetic particle inspection; Magnetization; Magnetic nanoparticles; Voltage; Nanoparticle; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002097083,0.0002195172,0.0002827578,0.0002083486,0.0003108534,0.0003315791,0.00120854,0.0007502082,0.003523778],"category_scores_gemma":[0.0003332127,0.0002552007,0.0001615221,0.0001499284,0.0002762358,0.0003878483,0.0005091808,0.0007120981,0.001164335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002495345,"about_ca_system_score_gemma":0.0007335774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002434401,"about_ca_topic_score_gemma":0.0005087167,"domain_scores_codex":[0.9997532,0.00002426475,0.00000974565,0.00005992106,0.0001367812,0.00001603807],"domain_scores_gemma":[0.9997997,0.00006491187,0.00002964149,0.00002540195,0.00004426694,0.00003610699],"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":[0.00005425879,0.00004172503,0.0003522081,0.00005300144,0.00000770439,0.0001489931,0.00001926231,0.0003915161,0.9802623,0.001837,0.001351992,0.01548003],"study_design_scores_gemma":[0.0001219611,0.0008772229,0.003568647,0.00001908143,0.00002890389,0.003100342,0.00003637213,0.04136477,0.8858967,0.0009217925,0.06398619,0.00007803967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4443009,0.001640645,0.5206413,0.002590385,0.0007919377,0.001022174,0.001401228,0.007016604,0.02059484],"genre_scores_gemma":[0.4219485,0.0004148476,0.5552138,0.0010327,0.0001457193,0.0008414154,0.001022246,0.0002152641,0.01916545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003523778,"threshold_uncertainty_score":0.01178819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005628469757075129,"score_gpt":0.1874676182505637,"score_spread":0.1818391484934886,"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."}}