{"id":"W2995792216","doi":"10.1039/c9an01565e","title":"Addressing the presence of biogenic selenium nanoparticles in yeast cells: analytical strategies based on ICP-TQ-MS","year":2019,"lang":"en","type":"article","venue":"The Analyst","topic":"Selenium in Biological Systems","field":"Nursing","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Fundación para el Fomento en Asturias de la Investigación Científica Aplicada y la Tecnología; Ministerio de Ciencia e Innovación","keywords":"Selenium; Inductively coupled plasma mass spectrometry; Nanoparticle; Chemistry; Lysis; Inductively coupled plasma; Particle (ecology); Particle size; Mass spectrometry; Nanomaterials; Yeast; Silver nanoparticle; Transmission electron microscopy; Elemental analysis; Chromatography; Nanotechnology; Materials science; Inorganic chemistry; Organic chemistry; Biochemistry","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":[0.0009861919,0.0001815669,0.0003414754,0.00008515346,0.0001162816,0.0001151397,0.000656422,0.0001064639,0.0001734471],"category_scores_gemma":[0.00009237102,0.00008964653,0.0001597678,0.0007803625,0.000285946,0.00009055156,0.00006180842,0.000261457,0.0001333071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005696242,"about_ca_system_score_gemma":0.00003457895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001147025,"about_ca_topic_score_gemma":0.0002140889,"domain_scores_codex":[0.9977586,0.0006821725,0.0004545679,0.0003192614,0.0004013422,0.0003840502],"domain_scores_gemma":[0.9981267,0.0008430092,0.0001952145,0.0007231414,0.00006265854,0.00004921569],"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":[0.0005697314,0.0002056813,0.03600289,0.00007120941,0.00006419015,0.000002264624,0.0006108402,0.01946913,0.941218,0.0006946951,0.000739996,0.000351379],"study_design_scores_gemma":[0.00120951,0.0006187025,0.09572986,0.0004591211,0.0002471147,0.000006781412,0.005700396,0.2984489,0.5951932,0.000743431,0.001145019,0.0004979412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954518,0.0001908482,0.00002292612,0.0009113388,0.0002475574,0.0003466056,0.00001297417,0.0000378573,0.002778076],"genre_scores_gemma":[0.9995773,0.000003114349,0.0000322111,0.0001826316,0.00009915035,0.00001127393,0.000004352467,0.000014567,0.00007540028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3460248,"threshold_uncertainty_score":0.3655679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06887534161979299,"score_gpt":0.3106112404877411,"score_spread":0.2417358988679481,"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."}}