{"id":"W2951254923","doi":"10.3897/rio.5.e36152","title":"Training and hackathon on building biodiversity knowledge graphs","year":2019,"lang":"en","type":"article","venue":"Research Ideas and Outcomes","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Agriculture and Agri-Food Canada","funders":"","keywords":"Computer science; Biodiversity; Knowledge graph; Popularity; Identifier; World Wide Web; Knowledge management; Data science; Information retrieval; Ecology; Political science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004704309,0.0000992362,0.0001442807,0.0000822704,0.0001960035,0.00003849786,0.0000898233,0.00006513706,0.000005168243],"category_scores_gemma":[0.00008287786,0.00008095774,0.00004293157,0.00006240656,0.0001154817,9.187237e-7,0.0002646823,0.0001023418,0.00001204269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006394116,"about_ca_system_score_gemma":0.00002796668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002888536,"about_ca_topic_score_gemma":0.00002234656,"domain_scores_codex":[0.9991646,0.00006541739,0.00007706515,0.0003068495,0.0001119108,0.0002740909],"domain_scores_gemma":[0.9995863,0.00007520334,0.00001667055,0.0001641956,0.00006084519,0.00009676732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006404748,0.00004588825,0.7951473,0.00005826542,0.0001374233,0.000003736835,0.0006630789,0.000003528248,0.1726154,0.007650721,0.0009158091,0.02269484],"study_design_scores_gemma":[0.001166963,0.0008034901,0.8989086,0.0000301114,0.00001377998,0.000007297683,0.001218874,0.0000309747,0.01021326,0.003431308,0.08386146,0.000313899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923884,0.005299983,0.000002304585,0.0002706086,0.000075584,0.0001461621,0.00001374432,0.000002524044,0.001800668],"genre_scores_gemma":[0.9971095,0.002104816,0.0001457924,0.0000909173,0.00003168351,0.000003885369,0.000003530074,0.000006121373,0.000503724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1624021,"threshold_uncertainty_score":0.3301361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0677511620902073,"score_gpt":0.3562778678932562,"score_spread":0.2885267058030488,"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."}}