{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006968148,0.0003665211,0.0003040869,0.0004429929,0.0002160998,0.0004574201,0.000468042,0.0003654613,0.0006361106],"category_scores_gemma":[0.0007908873,0.0003105235,0.000263871,0.0002985137,0.0005914318,0.0005531022,0.0003732916,0.0009267507,0.0004407535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495134,"about_ca_system_score_gemma":0.0004164512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003476304,"about_ca_topic_score_gemma":0.0004154421,"domain_scores_codex":[0.999589,0.00007116792,0.0000251942,0.0001060042,0.0001473971,0.00006125447],"domain_scores_gemma":[0.9996723,0.0001551169,0.00004801124,0.00002471612,0.00006960338,0.00003029137],"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":[0.00006852957,0.000007856928,0.00009296001,0.00001802322,0.000002371407,0.00001239885,0.000006056236,0.0001057352,0.9953506,0.0001134891,0.00002830371,0.004193662],"study_design_scores_gemma":[0.0000135119,0.00009650376,0.0004327361,0.000002059697,0.00000718724,0.00009684941,0.00000372926,0.00231918,0.996439,0.00008317986,0.0004979494,0.000008103134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7431137,0.00271097,0.2489095,0.0004785142,0.000143464,0.0001886431,0.0002246876,0.001425705,0.002804935],"genre_scores_gemma":[0.8770389,0.001406055,0.1186222,0.000588668,0.0001165183,0.0001636385,0.0002285669,0.0001328349,0.001702595],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0006968148,"threshold_uncertainty_score":0.003685117,"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."}}