{"id":"W4387765682","doi":"10.1111/cobi.14205","title":"Identifying global conservation priorities for terrestrial vertebrates based on multiple dimensions of biodiversity","year":2023,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Biodiversity; Convention on Biological Diversity; Geography; Environmental resource management; Prioritization; Measurement of biodiversity; Endangered species; Biodiversity conservation; Ecology; Biology; Environmental science; Habitat; Business","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":[0.0001700536,0.00008118006,0.0001182877,0.00003601056,0.0001697565,0.00001175211,0.0001015676,0.00008821928,0.001265089],"category_scores_gemma":[0.0005275498,0.00007190039,0.00005771682,0.0003247125,0.0002190454,0.00007075728,0.00006280152,0.00002913187,0.0002693521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001603714,"about_ca_system_score_gemma":0.00002171158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007509285,"about_ca_topic_score_gemma":0.0007035987,"domain_scores_codex":[0.9993276,0.00005268189,0.0001792827,0.0001920814,0.00008622194,0.000162159],"domain_scores_gemma":[0.9993308,0.000380888,0.0001061879,0.0001211464,0.00002613358,0.0000348414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001225165,0.00002810192,0.9425372,0.000008525589,0.000005988612,3.765978e-7,0.0000294571,0.00002943369,0.01429379,0.000858736,0.0418986,0.000187306],"study_design_scores_gemma":[0.001443673,0.0001489652,0.9501293,0.000009940487,0.00001367284,3.363507e-7,0.0004023124,0.01558031,0.003623943,0.0007505058,0.02777674,0.0001202635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993877,0.000004810041,0.0007213618,0.003501457,0.0004266429,0.0002863815,0.0008305865,0.00008495131,0.0002668119],"genre_scores_gemma":[0.9972431,0.00001467351,0.0001329892,0.001370329,0.00001421661,0.00002798853,0.001150231,0.000002543953,0.00004391041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01555088,"threshold_uncertainty_score":0.9996479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09237554912479974,"score_gpt":0.3009678295315295,"score_spread":0.2085922804067297,"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."}}