{"id":"W4403099717","doi":"10.1093/clinchem/hvae099","title":"An Interference That Makes You Blue?","year":2024,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Methemoglobinemia and Tumor Lysis Syndrome","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interference (communication); Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003665003,0.0006411356,0.0005515597,0.0006189249,0.001994922,0.001237983,0.0005814036,0.003791627,0.01172872],"category_scores_gemma":[0.003067423,0.0002680396,0.0004817988,0.0004713246,0.0009471722,0.002163772,0.0008451592,0.003324297,0.005286873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000949336,"about_ca_system_score_gemma":0.0006960781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003825617,"about_ca_topic_score_gemma":0.005548767,"domain_scores_codex":[0.9993914,0.0001131036,0.00004029512,0.0000977899,0.000180984,0.0001763806],"domain_scores_gemma":[0.9994507,0.00009203551,0.0001027612,0.00001859971,0.0001490594,0.0001868571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"case_report","study_design_scores_codex":[0.000400792,0.001229298,0.04958853,0.00101251,0.0001343432,0.3295966,0.004735143,0.0001358612,0.006528066,0.007488377,0.3920359,0.2071146],"study_design_scores_gemma":[0.00006735996,0.0004861573,0.01742878,0.0009865545,0.00008345846,0.7166139,0.007919697,0.0003856255,0.001570612,0.00554366,0.2488314,0.00008276382],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.254119,0.1053493,0.007549824,0.4389724,0.03847333,0.0003679682,0.0005507857,0.001032942,0.1535845],"genre_scores_gemma":[0.5744107,0.05276995,0.005935873,0.2571001,0.01210136,0.0001283404,0.0004411715,0.0002340469,0.09687844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01172872,"threshold_uncertainty_score":0.03923643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07757105599752286,"score_gpt":0.4026501447393015,"score_spread":0.3250790887417787,"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."}}