{"id":"W2765251120","doi":"10.2174/2213240602666150722232236","title":"Comprehensive Multidimensional Chromatography","year":2015,"lang":"en","type":"article","venue":"Current Chromatography","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Two-dimensional gas; Multidimensional systems; Multidimensional analysis; Two-dimensional chromatography; Separation (statistics); Multidimensional data; Gas chromatography; Computer science; Biochemical engineering; Chromatography; Chemistry; Mathematics; Machine learning; Engineering; Data mining","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001394924,0.0007304194,0.0007023621,0.0004077576,0.000262244,0.00009516769,0.0006194498,0.0002968631,0.0011685],"category_scores_gemma":[0.00005016257,0.000696863,0.0009993946,0.001481752,0.0006026708,0.0002854178,0.0002398497,0.0006497015,0.0003088267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005323607,"about_ca_system_score_gemma":0.0001442552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001775857,"about_ca_topic_score_gemma":0.0000016547,"domain_scores_codex":[0.9962061,0.00004760177,0.0007625044,0.00097098,0.001066664,0.0009461623],"domain_scores_gemma":[0.9970109,0.0001405375,0.0002711409,0.0009502531,0.0004212727,0.001205827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000834602,0.008267173,0.4483612,0.005802372,0.003402613,0.0005432514,0.001773596,0.000234745,0.2437974,0.02692685,0.2378723,0.02218395],"study_design_scores_gemma":[0.006178527,0.000120941,0.004271487,0.0007299501,0.0003924186,0.0003148639,0.001307991,0.001507037,0.0890246,0.004808417,0.8887131,0.00263063],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587812,0.01139028,0.0004431374,0.00008885276,0.0006790781,0.0001568099,0.0001353736,0.001066955,0.02725831],"genre_scores_gemma":[0.9972956,0.0002658306,0.001208492,0.0001083844,0.0005015471,0.00007950322,0.000397354,0.0000885616,0.00005470743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6508409,"threshold_uncertainty_score":0.9997446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04146388567232589,"score_gpt":0.2856897631371469,"score_spread":0.244225877464821,"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."}}