{"id":"W2013049722","doi":"10.1016/j.chroma.2007.01.116","title":"Characterizing property distributions of polymeric nanogels by size-exclusion chromatography","year":2007,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nanogel; Molar mass; Size-exclusion chromatography; Chemistry; Calibration; Polymer; Volume fraction; Particle size; Calibration curve; Molar mass distribution; Particle (ecology); Analytical Chemistry (journal); Chromatography; Biological system; Detection limit; Organic chemistry; Statistics; Physical chemistry; Mathematics","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":[0.0003264479,0.0002813146,0.000198543,0.0005002095,0.0001765802,0.000504186,0.0001898693,0.0002162661,0.0006038898],"category_scores_gemma":[0.0006592931,0.00017452,0.0001723712,0.0001977216,0.0002930125,0.0007316686,0.0002112739,0.0007208635,0.0002396567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000267988,"about_ca_system_score_gemma":0.0002702593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006040487,"about_ca_topic_score_gemma":0.0004854493,"domain_scores_codex":[0.9998561,0.00001480274,0.000009961151,0.00004226435,0.00004282234,0.00003399817],"domain_scores_gemma":[0.9996054,0.0001703153,0.00007914959,0.00002397863,0.00007935055,0.00004192565],"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":[0.00003169151,0.00001071459,0.0002524621,0.0000108596,0.000002925965,0.00001000974,0.00001220832,0.0001016245,0.998004,0.00005684459,0.00001917677,0.00148748],"study_design_scores_gemma":[0.00000478733,0.0000768196,0.002347044,0.000002613193,0.000006753864,0.00005788796,0.00002039846,0.001889054,0.9950417,0.0001173979,0.0004306457,0.000005039661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666111,0.0007808048,0.03068455,0.00009122393,0.00001489394,0.00003424266,0.0002851733,0.0001542261,0.001343778],"genre_scores_gemma":[0.9874244,0.000687414,0.009939432,0.0001635168,0.00001114487,0.00007151762,0.0003768448,0.0001141994,0.001211434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006040487,"threshold_uncertainty_score":0.00202018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006535812241045164,"score_gpt":0.2296160362896326,"score_spread":0.2230802240485875,"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."}}