{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05716195,0.001125031,0.0007725231,0.007355102,0.001042329,0.002860735,0.002053371,0.0004902997,0.001901002],"category_scores_gemma":[0.06850915,0.0002573406,0.00099635,0.01127383,0.001071075,0.00147612,0.00305955,0.0009319538,0.0001426564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02322902,"about_ca_system_score_gemma":0.02507822,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5872363,"about_ca_topic_score_gemma":0.5932896,"domain_scores_codex":[0.9784583,0.01049676,0.001043578,0.0009624668,0.007480588,0.001558245],"domain_scores_gemma":[0.96523,0.007308506,0.007154492,0.002022848,0.01561669,0.002667492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008558984,0.0002857839,0.8368984,0.0004817859,0.0007200275,0.0002171639,0.001532271,0.02169571,0.0005367616,0.02637015,0.01711507,0.09329098],"study_design_scores_gemma":[0.0001501427,0.0005732257,0.9341925,0.0004061737,0.0002023741,0.00006928002,0.001721779,0.02881185,0.0008004197,0.001831236,0.03115969,0.00008124101],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7869134,0.004463495,0.03165462,0.004369744,0.0004333371,0.00649244,0.05956709,0.0004657185,0.1056401],"genre_scores_gemma":[0.9122102,0.0006207777,0.05715009,0.0003135164,0.00004216595,0.002955207,0.02368734,0.00007200824,0.002948668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5872363,"threshold_uncertainty_score":0.830389,"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."}}