{"id":"W4225140008","doi":"10.1039/d2an00200k","title":"2D and 3D maximum-quantum NMR and diffusion spectroscopy for the characterization of enzymatic reaction mixtures","year":2022,"lang":"en","type":"article","venue":"The Analyst","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CHIST-ERA; Fondation Aix-Marseille Universite; Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; Association Nationale de la Recherche et de la Technologie; Aix-Marseille Université","keywords":"Chemistry; Characterization (materials science); Nuclear magnetic resonance spectroscopy; Diffusion; Relaxation (psychology); Spectroscopy; NMR spectra database; Substrate (aquarium); Analytical Chemistry (journal); Spectral line; Biological system; Materials science; Chromatography; Nanotechnology; Organic chemistry; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001521376,0.00006592258,0.00009933244,0.00002551534,0.0005438185,0.00002601181,0.00009187117,0.00000782941,0.00007137729],"category_scores_gemma":[0.000001510387,0.00004059941,0.00003674594,0.0001127603,0.00004695953,0.00004114726,0.00005239549,0.0000806347,7.134082e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000790869,"about_ca_system_score_gemma":0.000009145619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001414355,"about_ca_topic_score_gemma":0.000003320149,"domain_scores_codex":[0.999569,0.00003481978,0.0001255329,0.0001075588,0.00007933073,0.00008376222],"domain_scores_gemma":[0.9995636,0.00008947695,0.0001367189,0.000180517,0.00001468098,0.00001498129],"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":[0.00003243347,0.00005524313,0.001390747,0.00001006856,0.00005964376,2.721085e-8,0.0004740267,0.00001313668,0.9579415,0.03801582,0.0001085473,0.001898758],"study_design_scores_gemma":[0.002697054,0.0007508371,0.1624663,0.00006084504,0.002188152,0.00001420454,0.009288942,0.1628193,0.3842885,0.2160142,0.05858687,0.0008248318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682371,0.000084087,0.02991004,0.001166644,0.00002857419,0.0003166136,0.00006622085,0.000007580986,0.0001831071],"genre_scores_gemma":[0.9993671,0.00003239929,0.0001055016,0.00004422095,0.0001064266,0.0001325274,0.00007430276,0.000007032389,0.0001304736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5736531,"threshold_uncertainty_score":0.4182667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008243876932076875,"score_gpt":0.2788542163205215,"score_spread":0.2706103393884446,"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."}}