{"id":"W4400249470","doi":"10.1111/ele.14461","title":"Multinational evaluation of genetic diversity indicators for the Kunming‐Montreal Global Biodiversity Framework","year":2024,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Forskningsråd; Vetenskapsrådet; Consejo Nacional de Ciencia y Tecnología; Svenska Forskningsrådet Formas; Agence Nationale de la Recherche","keywords":"Biodiversity; Genetic diversity; Ecology; Diversity (politics); Geography; Environmental resource management; Multinational corporation; Biology; Environmental science; Political science; Population; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004249564,0.00006456427,0.00006954567,0.00003759074,0.0002733367,0.000008404218,0.0002162961,0.00006517777,0.001376845],"category_scores_gemma":[0.00006135811,0.00005123501,0.000061871,0.0001572306,0.0002304266,0.00005840932,0.0003088404,0.00006055829,0.0001414029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002220689,"about_ca_system_score_gemma":0.000009869692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002386765,"about_ca_topic_score_gemma":0.0007557514,"domain_scores_codex":[0.9993028,0.00007005673,0.00008893295,0.000193568,0.0001964108,0.0001482275],"domain_scores_gemma":[0.999581,0.0002507934,0.00004780493,0.00009094084,0.000006687952,0.00002280127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001358565,0.00002830812,0.949834,0.000005455147,0.00008942081,0.000002542516,0.0002488596,0.001739669,0.00001288348,0.0001576479,0.03987216,0.007995391],"study_design_scores_gemma":[0.0002512013,0.0000392648,0.9911102,0.000001423585,0.0002256081,8.637851e-7,0.00004407869,0.004416928,0.00000413059,0.0009162718,0.002936154,0.00005388005],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904451,0.00003877289,0.00312893,0.004687931,0.0006116464,0.0004677243,0.00003821021,0.00002056948,0.0005610532],"genre_scores_gemma":[0.9983737,0.000005256276,0.0008313279,0.0006902819,0.00003449938,0.00002500133,0.00001266478,0.000001414835,0.00002586242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04127612,"threshold_uncertainty_score":0.999536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213776994573821,"score_gpt":0.2419030932976046,"score_spread":0.2297653233518664,"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."}}