{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005749789,0.0002477235,0.0001942148,0.00008104743,0.0005280706,0.0007259123,0.0003522097,0.0001350083,0.00003724763],"category_scores_gemma":[0.000181541,0.0001698176,0.0000483622,0.0001572989,0.0003115576,0.00003812897,0.00070596,0.000333418,0.00002822443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004844182,"about_ca_system_score_gemma":0.0002303344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004596295,"about_ca_topic_score_gemma":0.0009279412,"domain_scores_codex":[0.99856,0.00008177426,0.0002289276,0.0006289719,0.0002124044,0.0002879898],"domain_scores_gemma":[0.9993436,0.00006057286,0.0000456989,0.0003476564,0.00007947833,0.00012297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01055161,0.008331685,0.5182313,0.0005143696,0.00440375,0.001302751,0.01322263,0.001523526,0.1495723,0.05747365,0.01104929,0.2238232],"study_design_scores_gemma":[0.03025694,0.005723931,0.5199731,0.0009719768,0.0003082492,0.0009226185,0.01328346,0.1673031,0.1452787,0.001774993,0.1106012,0.003601765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936931,0.0004778464,0.001300851,0.0005159208,0.0001490226,0.002275483,0.00006893596,0.00002737206,0.001491521],"genre_scores_gemma":[0.9962331,0.0001364597,0.001777948,0.0002107268,0.00008488756,0.0002071486,0.0000685591,0.00003048257,0.001250666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2202214,"threshold_uncertainty_score":0.6999989,"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."}}