{"id":"W3092791034","doi":"10.1088/1361-648x/abe649","title":"Multiscale modelling of magnetostatic effects on magnetic nanoparticles with application to hyperthermia","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Condensed Matter","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanorod; Materials science; Scaling; Condensed matter physics; Magnetic hyperthermia; Granularity; Nanoparticle; Anisotropy; Magnetite; Magnetic nanoparticles; Maghemite; Saturation (graph theory); Statistical physics; Physics; Nanotechnology; Mathematics; Computer science; Optics","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":[0.0002741403,0.0004219368,0.0004927839,0.0003045941,0.0003163633,0.0004817895,0.0007425761,0.0009362445,0.001011629],"category_scores_gemma":[0.001211194,0.0002708492,0.000528209,0.0002380512,0.0005561472,0.0005185377,0.0004733381,0.0004570108,0.0001237798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007903252,"about_ca_system_score_gemma":0.0006189564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006816801,"about_ca_topic_score_gemma":0.004344141,"domain_scores_codex":[0.9998988,0.0000322925,0.000004938859,0.00001494115,0.00003126895,0.00001785481],"domain_scores_gemma":[0.9996544,0.0001961578,0.00003701985,0.00003984177,0.00003832419,0.00003416125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001376425,0.00002159153,0.0002989039,0.00001930101,0.00001319116,0.00004620029,0.00002678232,0.9895857,0.005141316,0.003259916,0.00008953479,0.001483731],"study_design_scores_gemma":[0.000002626349,0.000004653918,0.00006860956,9.976399e-7,0.000001226623,0.000002537357,0.000001500932,0.9991444,0.0002277337,0.000429652,0.000114396,0.000001640293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4520381,0.0006945218,0.5290882,0.001006435,0.0001699073,0.000211483,0.0002954868,0.0008094808,0.01568639],"genre_scores_gemma":[0.9435561,0.0001999437,0.05435517,0.00007473215,0.00003635949,0.000161906,0.00007967542,0.0001381161,0.001398027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006816801,"threshold_uncertainty_score":0.01355428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006917100728492758,"score_gpt":0.1915833021292732,"score_spread":0.1846662014007805,"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."}}