{"id":"W4309625182","doi":"10.1038/s41597-022-01812-6","title":"An expert curated global legume checklist improves the accuracy of occurrence, biodiversity and taxonomic data","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Plant and Fungal Species Descriptions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Université de Montréal; Royal Botanical Gardens, Kew","keywords":"Checklist; Taxon; Taxonomic rank; Biodiversity; Biology; Global biodiversity; Ecology","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":[0.0005865273,0.00008079242,0.00007549892,0.00001998555,0.0006960809,0.0001560713,0.00266553,0.00002549931,0.00008448383],"category_scores_gemma":[0.0001142638,0.00006536578,0.00001508536,0.0001929016,0.0004183022,0.00005219642,0.004198066,0.00007905748,0.000006140677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001199343,"about_ca_system_score_gemma":0.0001156669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002024488,"about_ca_topic_score_gemma":0.0002419658,"domain_scores_codex":[0.9988829,0.00007574085,0.0001238773,0.0006220557,0.00014182,0.0001536377],"domain_scores_gemma":[0.9975122,0.00001062655,0.00009001853,0.002299785,0.00003209853,0.0000552905],"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":[0.00003516713,0.00007088405,0.001480184,0.000003271754,0.0000199204,9.351713e-7,0.00003004439,0.000001811873,0.3142278,0.00003640319,0.6818301,0.002263473],"study_design_scores_gemma":[0.000181892,0.00005247239,0.003437745,0.000001431437,0.00001826997,0.00001455389,0.000824159,0.001184809,0.002406535,0.000009145895,0.9917521,0.000116895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.728833,0.00115056,0.00009816226,0.0002574045,0.001112432,0.0001915796,0.2679929,0.00001151102,0.0003524611],"genre_scores_gemma":[0.8864,0.00008096365,0.0001816019,0.00009593127,0.00005411256,0.000003923587,0.1129669,0.000001825015,0.0002148088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3118213,"threshold_uncertainty_score":0.5353761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06531958014189927,"score_gpt":0.2958452711767843,"score_spread":0.2305256910348851,"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."}}