{"id":"W2794101634","doi":"10.1520/stp160220160136","title":"Visualization of Urea Treatments Using Micro-X-Ray Fluorescence Spectrometry","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"X-ray fluorescence; Visualization; Urea; Mass spectrometry; Fluorescence; Chemistry; Analytical Chemistry (journal); Materials science; Computer science; Chromatography; Physics; Data mining; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003654936,0.0004617564,0.000413842,0.0007026132,0.0003742048,0.0004617458,0.0004651585,0.0007827519,0.002508107],"category_scores_gemma":[0.0003969697,0.0002894132,0.0003319523,0.000424066,0.0002361311,0.0006448376,0.0002855636,0.001018804,0.0006705148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003692299,"about_ca_system_score_gemma":0.0002689396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001448101,"about_ca_topic_score_gemma":0.003291877,"domain_scores_codex":[0.9997026,0.00002572433,0.00001913888,0.00007916084,0.0001238692,0.00004949674],"domain_scores_gemma":[0.9996693,0.00009836265,0.00006079466,0.00002021569,0.0001326431,0.00001873814],"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":[0.00003082918,0.00001429187,0.0004203224,0.00009637497,0.000004927694,0.00008088404,0.00004507102,0.0001284906,0.9948548,0.0001491073,0.0002599458,0.0039149],"study_design_scores_gemma":[0.000006818566,0.0000886382,0.004526676,0.00002511133,0.00001486613,0.0001996266,0.0001100119,0.003194162,0.9847513,0.0001415232,0.006924065,0.00001720772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8007064,0.009583361,0.1669854,0.0009335596,0.000323169,0.0002877509,0.002912449,0.003352458,0.01491544],"genre_scores_gemma":[0.7849589,0.005949987,0.1939474,0.0005427518,0.00005322152,0.0004131782,0.001567782,0.0003721071,0.01219451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002508107,"threshold_uncertainty_score":0.008390427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645338580421894,"score_gpt":0.2726118209270499,"score_spread":0.2561584351228309,"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."}}