{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.05996387,0.003354587,0.006701445,0.01740249,0.007835189,0.02120647,0.009914666,0.0089541,0.6950373],"category_scores_gemma":[0.3051492,0.003784391,0.003591313,0.02202038,0.006550851,0.01728478,0.02047735,0.008840304,0.5060241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006921463,"about_ca_system_score_gemma":0.01922754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002640331,"about_ca_topic_score_gemma":0.004227417,"domain_scores_codex":[0.939511,0.01302869,0.01036271,0.01274133,0.01864227,0.005714075],"domain_scores_gemma":[0.6682616,0.05417367,0.02107825,0.1045748,0.1283928,0.02351897],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002400203,0.00002629672,0.0004115062,0.001407742,0.00005419806,0.0001023415,0.000193645,0.0001188137,0.0008665701,0.009533335,0.9708283,0.01621724],"study_design_scores_gemma":[0.0003452699,0.00004190808,0.001127595,0.00142968,0.00008259414,0.0003695729,0.0002961381,0.001745498,0.003136462,0.01829941,0.9729493,0.0001766091],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.001959014,0.001429286,0.1493868,0.03125735,0.09082159,0.008705099,0.5081986,0.1023737,0.1058686],"genre_scores_gemma":[0.04321814,0.002181198,0.2329895,0.01495674,0.0142101,0.02795176,0.3867914,0.1325455,0.1451557],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9400361,"threshold_uncertainty_score":0.4349923,"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."}}