{"id":"W3089744492","doi":"10.18383/j.tom.2020.00043","title":"A Perspective on Cell Tracking with Magnetic Particle Imaging","year":2020,"lang":"en","type":"article","venue":"Tomography","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Magnetic particle imaging; Magnetic resonance imaging; Tracking (education); Cell; Iron oxide nanoparticles; Nuclear magnetic resonance; Magnetic nanoparticles; Materials science; Nanotechnology; Nanoparticle; Chemistry; Physics; Medicine; Radiology","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":[0.0000188122,0.00009812653,0.00007550551,0.00003903981,0.00004171957,0.00005032608,0.00008311919,0.00001206307,0.0001560693],"category_scores_gemma":[0.000003819627,0.00009088425,0.00003347675,0.0004365556,0.00003514684,0.00007014436,0.000007454566,0.00007208931,0.00008322485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001104336,"about_ca_system_score_gemma":0.000003983773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002391163,"about_ca_topic_score_gemma":9.019694e-7,"domain_scores_codex":[0.9994992,0.000007765509,0.00009293496,0.0001484812,0.00009856339,0.0001530398],"domain_scores_gemma":[0.9997135,0.00001744519,0.00001339183,0.0001195904,0.0000316652,0.0001044445],"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":[0.0002574962,0.0004401322,0.02905263,0.0001533343,0.0000551175,0.00004724248,0.01139071,0.014635,0.8780193,0.01307442,0.003437178,0.04943743],"study_design_scores_gemma":[0.003485435,0.001122997,0.07219903,0.0000709687,0.0001175207,0.00001287393,0.004494496,0.1554154,0.7409133,0.0006279475,0.02033088,0.001209128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983286,0.0004385794,0.002042835,0.002410108,0.00002149815,0.0001959117,0.000007120789,0.0005703915,0.01102753],"genre_scores_gemma":[0.9988369,0.00001354672,0.0005152661,0.0005248851,0.00004260449,0.00002710807,0.000001977115,0.00002310308,0.00001456929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1407804,"threshold_uncertainty_score":0.3706152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006308436930934089,"score_gpt":0.1854292449515757,"score_spread":0.1791208080206416,"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."}}