{"id":"W4401061076","doi":"10.1021/acs.analchem.4c01390","title":"Development of a Simple Cost Effective Oxygenation System for In Vivo Solution State NMR in 10 mm NMR Tubes","year":2024,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ministère de l’Environnement, de la Protection de la nature et des Parcs; Krembil Foundation; Health Canada; Government of Ontario","keywords":"Chemistry; Simple (philosophy); Oxygenation; In vivo; Proton NMR; Nuclear magnetic resonance spectroscopy; Carbon-13 NMR; Combinatorial chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00124774,0.0008409799,0.0005742714,0.0003948778,0.0003622608,0.0004731795,0.0009745516,0.001044707,0.003224098],"category_scores_gemma":[0.0008199704,0.0006725887,0.0004259284,0.0002633299,0.0003565236,0.0007674145,0.0005299829,0.001537023,0.003214564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003244501,"about_ca_system_score_gemma":0.000555946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004095011,"about_ca_topic_score_gemma":0.001005199,"domain_scores_codex":[0.9995601,0.0000804997,0.00005504743,0.0001370844,0.0001111807,0.00005607953],"domain_scores_gemma":[0.9993922,0.0001536574,0.0001597516,0.00007563652,0.000126984,0.00009174759],"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":[0.00003815867,0.00003652028,0.0001011244,0.00007952168,0.000004127338,0.00004739137,0.0000234511,0.0001836643,0.9963284,0.0002074539,0.0001892075,0.002760992],"study_design_scores_gemma":[0.0000307035,0.0005430313,0.001029643,0.00003967727,0.00003457936,0.0002142161,0.00003724826,0.00362331,0.973349,0.0001412284,0.02092179,0.00003552783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3214645,0.001291065,0.6622539,0.001236414,0.0005677358,0.001884345,0.002197232,0.004305779,0.004798986],"genre_scores_gemma":[0.1975607,0.001600209,0.7856793,0.0004185292,0.0001140302,0.002758998,0.002976475,0.0004329966,0.008458942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003224098,"threshold_uncertainty_score":0.0107857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009667272419112754,"score_gpt":0.3160416151495334,"score_spread":0.3063743427304207,"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."}}