{"id":"W2021252522","doi":"10.1016/j.aca.2014.01.036","title":"Impact of calibrator concentrations and their distribution on accuracy of quadratic regression for liquid chromatography–mass spectrometry bioanalysis","year":2014,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Micropharma (Canada)","funders":"","keywords":"Bioanalysis; Chemistry; Chromatography; Mass spectrometry; Quadratic equation; Regression; Regression analysis; Liquid chromatography–mass spectrometry; Calibration; Analytical Chemistry (journal); Linear regression; Statistics; Mathematics","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":[0.02661289,0.001284597,0.001052287,0.001478191,0.001311186,0.003320658,0.001743761,0.003246698,0.001503991],"category_scores_gemma":[0.08814599,0.001176417,0.001193914,0.001286712,0.002186511,0.001484197,0.001745116,0.002223825,0.001188469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774051,"about_ca_system_score_gemma":0.001792478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004430523,"about_ca_topic_score_gemma":0.003030519,"domain_scores_codex":[0.9620427,0.01833382,0.001752641,0.007282077,0.009495086,0.001093786],"domain_scores_gemma":[0.9550687,0.0325886,0.00160275,0.004944073,0.005550454,0.0002454622],"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":[0.011341,0.0008558464,0.03446154,0.001318626,0.001367981,0.0007870244,0.001587701,0.05610565,0.67039,0.006631213,0.005641505,0.209512],"study_design_scores_gemma":[0.0002269953,0.000951282,0.01286762,0.0001526234,0.0005304937,0.0009264945,0.0001426487,0.1963265,0.7785497,0.002357798,0.006787756,0.0001800015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3531041,0.008880865,0.621381,0.002211549,0.0008520021,0.0004456964,0.0009506358,0.005966728,0.006207327],"genre_scores_gemma":[0.8620192,0.001768344,0.1279746,0.00175428,0.0001427446,0.0003808219,0.001095338,0.002416821,0.002447718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02661289,"threshold_uncertainty_score":0.1407441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505409720290778,"score_gpt":0.2993352588317176,"score_spread":0.2842811616288098,"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."}}