{"id":"W2062580687","doi":"10.1515/cclm.2002.030","title":"Turn-Around Time for Chemical and Endocrinology Analyzers Studied Using Simulation","year":2002,"lang":"en","type":"article","venue":"Clinical Chemistry and Laboratory Medicine (CCLM)","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bayer Canada; Bayer Corporation","keywords":"Spectrum analyzer; Computer science; Arrival time; Simulation; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001238651,0.0005820844,0.0007255106,0.0006780993,0.0006036432,0.00087409,0.0006731555,0.0008092055,0.003613422],"category_scores_gemma":[0.005308309,0.0004173623,0.0007829626,0.0003781576,0.0006467624,0.0005819128,0.0005112538,0.0008203103,0.0002624334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499401,"about_ca_system_score_gemma":0.001291541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390552,"about_ca_topic_score_gemma":0.005805462,"domain_scores_codex":[0.9994424,0.0001873954,0.00002052439,0.00007898251,0.00009828284,0.0001724368],"domain_scores_gemma":[0.9949666,0.003751204,0.0003790819,0.0002445355,0.00042059,0.0002380188],"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":[0.0001646156,0.00003525037,0.002643023,0.00001603934,0.00001765433,0.00005998584,0.0000535238,0.9924661,0.001095914,0.001557469,0.000130207,0.001760308],"study_design_scores_gemma":[0.00001495499,0.00005863659,0.0006757959,0.000005384473,0.00001682453,0.00002041383,0.00003689346,0.997017,0.001225965,0.0006768564,0.0002397536,0.00001153253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9087734,0.000259751,0.08043575,0.000278738,0.00005583401,0.0001186453,0.0003372851,0.0004734259,0.009267245],"genre_scores_gemma":[0.9908902,0.00008041555,0.007428462,0.00002565808,0.000003219329,0.00004947932,0.0001461276,0.00003729378,0.001339171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01390552,"threshold_uncertainty_score":0.02764916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06005072261306955,"score_gpt":0.3518834640188768,"score_spread":0.2918327414058073,"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."}}