{"id":"W4403537762","doi":"10.1016/j.esmoop.2024.103926","title":"164TiP MONSTAR-GLYCO: A multi-institutional prospective study harnessing glycomics and multi-omics on the j-glyconet and SCRUM-MONSTR platform","year":2024,"lang":"en","type":"article","venue":"ESMO Open","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Glycomics Network; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Scrum; Glycomics; Omics; Computer science; Computational biology; Biology; Bioinformatics; Software; Operating system; Software development; Glycan; Molecular biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002489776,0.0009667805,0.0007860179,0.0008696897,0.001736382,0.002004677,0.0009173921,0.000963782,0.009569269],"category_scores_gemma":[0.002292718,0.0005436012,0.0007232295,0.001501721,0.0009113177,0.001011015,0.004423031,0.00161426,0.006226114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105575,"about_ca_system_score_gemma":0.003247235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00567674,"about_ca_topic_score_gemma":0.005725285,"domain_scores_codex":[0.9987863,0.0003648554,0.00007566229,0.0002788132,0.0001439561,0.000350485],"domain_scores_gemma":[0.9970372,0.0002138322,0.000400598,0.000552564,0.0004718024,0.001324046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02816284,0.01028293,0.7898386,0.001127098,0.00141244,0.002763514,0.001916331,0.0008121582,0.01516593,0.002657162,0.05708046,0.08878066],"study_design_scores_gemma":[0.003231254,0.01183595,0.893289,0.0006517173,0.001266,0.003533074,0.004521499,0.002093947,0.005238459,0.002788831,0.07120515,0.0003452108],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457003,0.0008162329,0.00695267,0.001440251,0.0003039798,0.002566969,0.03332213,0.0005350485,0.008362398],"genre_scores_gemma":[0.9178495,0.001029072,0.01614162,0.004384165,0.000561896,0.004800091,0.04502668,0.0005619625,0.00964496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009569269,"threshold_uncertainty_score":0.03201234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05409124199152692,"score_gpt":0.339337415720969,"score_spread":0.2852461737294421,"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."}}