{"id":"W3196879910","doi":"10.1101/2021.08.31.458439","title":"A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Glycan; Glycomics; Lectin; Computational biology; Glycobiology; Biology; Biochemistry; Glycoprotein","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007156195,0.000458959,0.0004926801,0.0004582371,0.0001809879,0.0002100474,0.0004604507,0.0005745743,0.0001061752],"category_scores_gemma":[0.001479715,0.0005318292,0.000193343,0.0008185487,0.00008392608,0.00001228137,0.0006586524,0.0007355688,0.00002068638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001534558,"about_ca_system_score_gemma":0.000836051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003346038,"about_ca_topic_score_gemma":0.00008590551,"domain_scores_codex":[0.996916,0.0004335058,0.0006402599,0.001025716,0.0004713124,0.0005131866],"domain_scores_gemma":[0.997119,0.00004486132,0.0003745006,0.0009177478,0.001293312,0.000250555],"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":[0.0001147935,0.00009428737,0.008842736,0.0001881839,0.0001573624,0.00002268321,0.00002690752,0.0006588581,0.9890194,0.00003506863,0.0008330482,0.000006704992],"study_design_scores_gemma":[0.0003890299,0.0001818698,0.0171058,0.0002281854,0.00003246432,6.912006e-8,0.00002263228,0.0004244127,0.9432974,3.399568e-7,0.03778692,0.0005308982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924833,0.0007577835,0.004917703,0.000186835,0.0005219394,0.0007966737,0.00009294141,0.0001558488,0.0000869382],"genre_scores_gemma":[0.9899176,0.0003360214,0.008639776,0.0000816367,0.0002892646,0.0001676772,0.00001655698,0.0001300824,0.0004214119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04572199,"threshold_uncertainty_score":0.9997133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553389796566073,"score_gpt":0.2580303531434955,"score_spread":0.2424964551778347,"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."}}