{"id":"W3004244792","doi":"10.4230/dagrep.9.8.70","title":"Computational Proteomics (Dagstuhl Seminar 19351)","year":2019,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitetet i Bergen; Universiteit Gent; Radboud Universiteit; Kungliga Tekniska Högskolan; Eidgenössische Technische Hochschule Zürich; University of Toronto; Buck Institute for Research on Aging; Friedrich-Schiller-Universität Jena; Leids Universitair Medisch Centrum; Universiteit Leiden; Princeton University; Université du Luxembourg; Dartmouth College","keywords":"Breakout; Session (web analytics); Identification (biology); Computer science; Proteome; Data science; Proteomics; World Wide Web; Bioinformatics; 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":[0.009679998,0.002435585,0.001195025,0.00231753,0.001650363,0.006882982,0.002434431,0.002710535,0.1910692],"category_scores_gemma":[0.01197786,0.0007625279,0.001682309,0.001859979,0.0008732504,0.003184875,0.007206806,0.005070793,0.1469161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003414877,"about_ca_system_score_gemma":0.003302593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001216875,"about_ca_topic_score_gemma":0.001830035,"domain_scores_codex":[0.995905,0.0008837851,0.0001892398,0.0008908352,0.001390814,0.0007404264],"domain_scores_gemma":[0.9943637,0.001011546,0.0001694135,0.0005062539,0.001470235,0.002478958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002523499,0.0001224695,0.0001478377,0.0001913515,0.000029045,0.00008776153,0.0001212821,0.0007902788,0.001568366,0.01225856,0.8947466,0.08968414],"study_design_scores_gemma":[0.000104358,0.0001130991,0.0009079014,0.0001959595,0.00001677566,0.00009718671,0.00007085044,0.001442946,0.001704758,0.01712566,0.9781876,0.00003282716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01602436,0.0420389,0.2198572,0.1394238,0.1689182,0.002123775,0.02732305,0.01771975,0.366571],"genre_scores_gemma":[0.05545895,0.01770015,0.06901005,0.01519404,0.03313973,0.001999388,0.02671448,0.01123195,0.7695514],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1910692,"threshold_uncertainty_score":0.6391903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050825642027453,"score_gpt":0.2682828677345903,"score_spread":0.2577746113143158,"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."}}