{"id":"W2151092984","doi":"10.1002/jssc.201200818","title":"Selectivity tuning via temperature pulsing using low thermal mass liquid chromatography and monolithic columns","year":2013,"lang":"en","type":"article","venue":"Journal of Separation Science","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Selectivity; Resolution (logic); Chromatography; Chemistry; Elution; Capillary action; Analytical Chemistry (journal); Temperature gradient; Thermal; High-performance liquid chromatography; Materials science; Thermodynamics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004507143,0.0001481351,0.0002256567,0.000154502,0.0004432112,0.0003723658,0.0002576705,0.0001187836,0.0001844429],"category_scores_gemma":[0.0000714479,0.0001270504,0.0001043284,0.0008442892,0.0004876403,0.001422087,0.00003729844,0.0003863658,0.000002256234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006434895,"about_ca_system_score_gemma":0.0001957912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001398791,"about_ca_topic_score_gemma":7.431541e-7,"domain_scores_codex":[0.998538,0.00002150664,0.0003884319,0.0002255561,0.0005362012,0.0002902595],"domain_scores_gemma":[0.9987069,0.00005327697,0.0003715911,0.0001499807,0.0004770753,0.0002411393],"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":[0.00001940933,0.0000311897,0.005014731,0.00003910018,0.0000189052,0.000005637683,0.0001964886,0.0002317291,0.9943133,0.00001100077,0.00002134445,0.00009717081],"study_design_scores_gemma":[0.0002702003,0.00005678072,0.0036759,0.0001573695,0.00003706301,0.000311732,0.0002925561,0.02969184,0.9650171,0.0002568355,0.00002637015,0.0002063012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957564,0.0001489727,0.002770744,0.00009736709,0.00004351867,0.00003581052,0.000001133268,0.00001737062,0.00112871],"genre_scores_gemma":[0.9983743,0.00001312979,0.001359128,0.00006106199,0.0001500645,0.000001341713,5.428328e-7,0.000008268577,0.00003216749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02946011,"threshold_uncertainty_score":0.5180964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101353783608425,"score_gpt":0.2681451288269575,"score_spread":0.258009750466115,"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."}}