{"id":"W1979757279","doi":"10.1021/ac702119d","title":"Spectral Editing of Organic Mixtures into Pure Components Using NMR Spectroscopy and Ultraviscous Solvents","year":2007,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chemistry; Solvent; Molecule; Proton; Diffusion; Spectral line; Proton NMR; Viscosity; Spectroscopy; NMR spectra database; Macromolecule; Range (aeronautics); Nuclear magnetic resonance spectroscopy; Two-dimensional nuclear magnetic resonance spectroscopy; Chemical physics; Analytical Chemistry (journal); Organic chemistry; Thermodynamics; Stereochemistry","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":[0.0006970007,0.00118298,0.0006413485,0.0008470257,0.000466301,0.0008604451,0.0006962662,0.0005924776,0.002253811],"category_scores_gemma":[0.0008136014,0.0006400449,0.0005105114,0.0007313005,0.0007184073,0.001264201,0.001168579,0.002160447,0.001872675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002813664,"about_ca_system_score_gemma":0.0003860724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002044445,"about_ca_topic_score_gemma":0.0005526464,"domain_scores_codex":[0.9994413,0.0001082376,0.00004033073,0.0001684051,0.0001672507,0.00007436614],"domain_scores_gemma":[0.9995883,0.0001238381,0.00009450481,0.0001035842,0.00006123193,0.00002858836],"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":[0.00006077309,0.00002337865,0.00005567973,0.0001495836,0.00001164623,0.0001239628,0.00004780022,0.0001354267,0.984385,0.000721015,0.0001887818,0.01409694],"study_design_scores_gemma":[0.00001052715,0.0001335355,0.000365019,0.00003213658,0.0000167714,0.0003068964,0.00003384621,0.001334004,0.9785289,0.0007022083,0.01850697,0.00002919257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172646,0.006179523,0.8624629,0.0003278066,0.0003881271,0.0006036228,0.001043165,0.003232777,0.008497404],"genre_scores_gemma":[0.2741663,0.01396212,0.6967287,0.0007476114,0.0002291301,0.001851057,0.002088488,0.001279122,0.008947507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002253811,"threshold_uncertainty_score":0.00753969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029476101633794,"score_gpt":0.3161206517674269,"score_spread":0.305825890751089,"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."}}