{"id":"W3211188797","doi":"10.1021/jacs.1c07429","title":"Identification and Quantification of Glycans in Whole Cells: Architecture of Microalgal Polysaccharides Described by Solid-State Nuclear Magnetic Resonance","year":2021,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Centre National de la Recherche Scientifique; U.S. Department of Energy; National Science Foundation","keywords":"Chemistry; Cellulose; Polysaccharide; Glycan; Solid-state nuclear magnetic resonance; Cell wall; Xylan; Nuclear magnetic resonance spectroscopy; Magic angle spinning; Bacterial cellulose; Sporopollenin; Chemical engineering; Biochemistry; Organic chemistry; Botany; Nuclear magnetic resonance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001135093,0.0000932402,0.0002643814,0.00001230373,0.00003440308,0.00001229115,0.0002698698,0.00004559958,0.00001271399],"category_scores_gemma":[0.0000549324,0.00007680133,0.000179261,0.000295453,0.0004099545,0.00005298725,0.00007144116,0.0002918938,1.964266e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005953145,"about_ca_system_score_gemma":0.00004460402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003573448,"about_ca_topic_score_gemma":0.000001937103,"domain_scores_codex":[0.9990045,0.00002131966,0.0005203675,0.0001512204,0.0001802016,0.000122369],"domain_scores_gemma":[0.998684,0.0000739854,0.0008043525,0.0002571186,0.0001390536,0.00004152108],"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":[0.00003019865,0.0001055775,0.0003191878,0.00004583042,0.00001165464,3.254902e-7,0.0003534665,0.0000301357,0.9908123,0.00001718254,0.0009754623,0.007298669],"study_design_scores_gemma":[0.00018784,0.00001468598,0.0005124183,0.00008499817,0.00002540178,0.00001613374,0.0005122923,0.0002771012,0.9953318,0.0009139127,0.002050274,0.00007312559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954127,0.001076312,0.002093338,0.001272891,0.000006506349,0.00004030054,0.00007820198,0.000007034905,0.00001273583],"genre_scores_gemma":[0.9903209,0.0006262434,0.008804838,0.0000860047,0.0000194689,0.000003181929,0.000003430657,0.00001498466,0.0001209303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007225544,"threshold_uncertainty_score":0.3131868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007114409283537237,"score_gpt":0.251850182697177,"score_spread":0.2447357734136398,"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."}}