{"id":"W4412565401","doi":"10.1248/cpb.c25-00243","title":"Deciphering Glycan Dynamics through Nonlinear Correlation Analysis","year":2025,"lang":"en","type":"article","venue":"Chemical and Pharmaceutical Bulletin","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Canadian Glycomics Network; Japan Society for the Promotion of Science; Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Nonlinear system; Dynamics (music); Correlation; Glycan; Statistical physics; Biological system; Computer science; Mathematics; Chemistry; Biology; Physics; Geometry; Biochemistry","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":[0.0001183269,0.0001208893,0.000146109,0.00004172832,0.00007492566,0.00003399558,0.0001125948,0.000154497,0.0004360578],"category_scores_gemma":[0.0001377016,0.000112692,0.00009606891,0.0003028686,0.0001401871,0.000001998432,0.0001607654,0.0001925574,0.00002977376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002126263,"about_ca_system_score_gemma":0.00002439055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001474998,"about_ca_topic_score_gemma":0.00000480163,"domain_scores_codex":[0.9991091,0.00003804982,0.0001829069,0.0003250913,0.000122778,0.0002221004],"domain_scores_gemma":[0.9996216,0.00003863903,0.00002297842,0.0001595568,0.00006677108,0.00009047172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001657157,0.0005202583,0.03278951,0.0002556898,0.001515642,0.00001800839,0.00007056371,0.000658522,0.8704846,0.008518236,0.01239926,0.07111258],"study_design_scores_gemma":[0.001243162,0.00004899337,0.0008127862,0.00001864711,0.0001954203,0.000005444407,0.00003652125,0.05689914,0.3588901,0.0004422591,0.581134,0.0002735353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9319223,0.001003408,0.0467485,0.00470715,0.00008950989,0.0002458298,0.00002913644,0.00003283315,0.01522132],"genre_scores_gemma":[0.9934262,0.0002380964,0.002794043,0.0009922337,0.00008409516,0.0000172676,0.0002575573,0.000008814306,0.002181663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5687348,"threshold_uncertainty_score":0.4774527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268559357173753,"score_gpt":0.3255879725787723,"score_spread":0.3129023790070347,"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."}}