{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004575529,0.000159412,0.0003934464,0.00001852624,0.00002439088,0.00005216195,0.0001915008,0.0002110224,0.002340157],"category_scores_gemma":[0.0003450433,0.000122364,0.0002545363,0.0001316006,0.000165872,0.00005787425,0.00004977161,0.0005831223,0.0003707684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003012037,"about_ca_system_score_gemma":0.0001192052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001472888,"about_ca_topic_score_gemma":1.633246e-7,"domain_scores_codex":[0.9986264,0.00003122297,0.0004280649,0.0005066945,0.0001918495,0.0002157893],"domain_scores_gemma":[0.9987419,0.0002292977,0.00004247402,0.0006076664,0.00003985623,0.0003387732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007699173,0.002723614,0.4975948,0.005609109,0.001100633,0.007551775,0.0005315196,0.000002376875,0.3145266,0.0005337258,0.04882665,0.1202292],"study_design_scores_gemma":[0.003921494,0.001276434,0.0329188,0.003342448,0.001502601,0.004410181,0.0009518004,0.006677632,0.7242689,0.002359053,0.2168859,0.001484761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815533,0.0006142379,0.0001488241,0.001125336,0.0003605633,0.00007026886,0.000009391872,0.0002493376,0.01586873],"genre_scores_gemma":[0.9833719,0.0000781948,0.0004054225,0.0003185622,0.000689621,0.000009290434,0.00005031964,0.00002606625,0.01505063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.464676,"threshold_uncertainty_score":0.9985718,"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."}}