{"id":"W1967516933","doi":"10.1016/j.chroma.2006.06.113","title":"A multiple chemical equilibria approach to modeling and interpreting the separation of amino acid enantiomers by chiral ligand-exchange chromatography","year":2006,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Pfizer","keywords":"Chemistry; Potentiometric titration; Elution; Equilibrium constant; Ligand (biochemistry); Enantiomer; Phase (matter); Chromatography; Dispersion (optics); Chemical equilibrium; Titration; Thermodynamics; Physical chemistry; Stereochemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008829849,0.0007511055,0.0008864322,0.0006228309,0.0007931942,0.001466223,0.001951649,0.001380108,0.001344073],"category_scores_gemma":[0.001840905,0.000681897,0.001077377,0.0005082185,0.0006814779,0.001257783,0.0006523262,0.001145349,0.0002993739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001257396,"about_ca_system_score_gemma":0.0016953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0134119,"about_ca_topic_score_gemma":0.008043681,"domain_scores_codex":[0.9997622,0.00009712479,0.00001643603,0.00003949822,0.00006096148,0.0000236426],"domain_scores_gemma":[0.9994337,0.0003740611,0.00005783566,0.00002888719,0.00008098283,0.00002455527],"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":[0.00003809921,0.00005570191,0.0002737391,0.00005633465,0.00006295382,0.000102157,0.00004643827,0.9557421,0.007904036,0.02823997,0.0003394436,0.007138873],"study_design_scores_gemma":[0.000005302617,0.000006735644,0.00001848192,0.000001352118,0.000006765832,0.000007263699,0.000004207584,0.9951142,0.001410869,0.003021192,0.0003983754,0.000005178523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03393594,0.0005204603,0.9600959,0.000541883,0.00005637258,0.00006977541,0.0001179941,0.0004813467,0.004180345],"genre_scores_gemma":[0.7238848,0.001413279,0.2664076,0.0002444241,0.0001259365,0.0003862528,0.0001792507,0.0002541429,0.007104357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0134119,"threshold_uncertainty_score":0.02666771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008001704216152676,"score_gpt":0.2298359469176298,"score_spread":0.2218342427014771,"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."}}