{"id":"W2992521260","doi":"10.1093/database/baz123","title":"Reactome and ORCID—fine-grained credit attribution for community curation","year":2019,"lang":"en","type":"article","venue":"Database","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Human Genome Research Institute; National Institutes of Health; Canada First Research Excellence Fund; National Institute of General Medical Sciences; European Molecular Biology Laboratory; Alfred P. Sloan Foundation","keywords":"Computer science; World Wide Web; Annotation; Attribution; Visibility; Interface (matter); Information retrieval; Data curation; Data science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000319573,0.00007920837,0.00007927873,0.00001664398,0.00009139768,0.00002158511,0.0000846791,0.00007716617,0.000009875593],"category_scores_gemma":[0.00004205283,0.00007424899,0.00002689811,0.00003008662,0.00002444715,0.000009733933,0.000105534,0.00008710843,0.00001065446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006660693,"about_ca_system_score_gemma":0.00002067835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003550007,"about_ca_topic_score_gemma":0.0000680068,"domain_scores_codex":[0.9995719,0.00002826043,0.0001288683,0.0001035531,0.00004183966,0.0001255864],"domain_scores_gemma":[0.9994703,0.00002076326,0.00006072737,0.000351961,0.00005034421,0.0000458933],"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":[0.0003764718,0.0001054041,0.001984081,0.0002231433,0.00006787079,4.62588e-7,0.0001644212,0.00002805181,0.9397679,0.004321115,0.03778785,0.01517321],"study_design_scores_gemma":[0.004730065,0.00117314,0.006147703,0.00007406812,0.0000756083,0.00003419381,0.0003882808,0.01454803,0.08627833,0.001214964,0.8845983,0.0007373384],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945971,0.0001662076,0.05148075,0.0001921894,0.0001892651,0.0003983065,0.001312191,0.00001083552,0.0002792482],"genre_scores_gemma":[0.9774829,0.00005391016,0.001920758,0.000253067,0.0001827614,0.00001764608,0.01983727,0.000008922612,0.0002427238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8534896,"threshold_uncertainty_score":0.3027786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175887654388006,"score_gpt":0.2596120624985189,"score_spread":0.2420232970597183,"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."}}