{"id":"W4308381996","doi":"10.1016/j.chroma.2022.463636","title":"Leveraging multi-mode microextraction and liquid chromatography stationary phases for quantitative analysis of neurotoxin β-N-methylamino-L-alanine and other non-proteinogenic amino acids","year":2022,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"PerkinElmer Biosignal","funders":"University of Toledo; American Chemical Society","keywords":"Chemistry; Derivatization; Chromatography; Hydrophilic interaction chromatography; Extraction (chemistry); Mass spectrometry; Detection limit; Alanine; High-performance liquid chromatography; Amino acid","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004295465,0.0003699262,0.0009174799,0.001329119,0.0003398549,0.00005168103,0.0002860292,0.0001169751,0.0003197096],"category_scores_gemma":[0.00006727646,0.0003648365,0.001039073,0.001680517,0.0003586621,0.0002686269,0.00009293409,0.0004228133,1.088593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003073893,"about_ca_system_score_gemma":0.0000742648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004508191,"about_ca_topic_score_gemma":0.000005195427,"domain_scores_codex":[0.9975206,0.00008354604,0.001090413,0.0004280566,0.0005301452,0.0003471916],"domain_scores_gemma":[0.99749,0.0004681029,0.00129979,0.0002691647,0.000260001,0.0002128692],"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":[0.001135519,0.0007109385,0.04453964,0.0005481543,0.005608599,0.00003584481,0.001050742,0.0003173132,0.9456767,0.00009356025,0.0001047929,0.0001781518],"study_design_scores_gemma":[0.01022107,0.003021148,0.03300817,0.0004094215,0.01140396,0.0009402583,0.0101989,0.0213613,0.9038283,0.0006222512,0.003509535,0.001475678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987623,0.002899475,0.008551756,0.00005407478,0.00004438314,0.0002059922,0.0004815067,0.00002881557,0.0001110206],"genre_scores_gemma":[0.9925132,0.0001841148,0.006976438,0.0000892668,0.00005010081,0.00006783284,0.00005585491,0.00004400389,0.00001919739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04184843,"threshold_uncertainty_score":0.9998804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261701963083039,"score_gpt":0.2972339362319962,"score_spread":0.2746169166011658,"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."}}