{"id":"W4220896579","doi":"10.1038/s41467-022-28778-8","title":"Measuring protected-area effectiveness using vertebrate distributions from leech iDNA","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Autoritatea Natională pentru Cercetare Stiintifică; Directorate for Biological Sciences; Chinese Academy of Sciences; Leverhulme Trust; Harvard University; National Natural Science Foundation of China; FAS Division of Science, Harvard University; Harvard Global Institute; Ohio University","keywords":"Vertebrate; Mammal; Biodiversity; Species richness; Ecology; Occupancy; Protected area; Biology; Invertebrate; Global biodiversity; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005258221,0.0001964451,0.0001632689,0.001842697,0.0003413975,0.0004020065,0.0002600347,0.0001617022,0.000824895],"category_scores_gemma":[0.0009735039,0.0001070161,0.0001648726,0.001224488,0.0003784989,0.0003938986,0.0005364059,0.0001454228,0.000140303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651489,"about_ca_system_score_gemma":0.0001940886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01172619,"about_ca_topic_score_gemma":0.04840191,"domain_scores_codex":[0.9997156,0.00007201477,0.00002478498,0.00009940549,0.00005285623,0.00003527814],"domain_scores_gemma":[0.9990199,0.0001776253,0.0005136374,0.00008648763,0.0001116197,0.00009079641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002657176,0.000009202635,0.9938573,0.00001710423,0.00003748362,0.00001737343,0.0001493131,0.0004696652,0.002257442,0.00003607804,0.00002962584,0.003092884],"study_design_scores_gemma":[9.536144e-7,0.00001936127,0.9988136,0.000004116789,0.000008320108,0.00002157131,0.000192389,0.000523596,0.0002661758,0.00002958076,0.0001178083,0.000002647464],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988585,0.00006642595,0.0003542836,0.000005215089,8.157349e-7,0.000004622582,0.0003927211,0.000004592667,0.000312921],"genre_scores_gemma":[0.9990145,0.00002161965,0.0005351172,0.000005151273,0.000001098798,0.000009747982,0.000325935,0.000001029301,0.0000858133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01172619,"threshold_uncertainty_score":0.02331591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04321149508589821,"score_gpt":0.2512092763957954,"score_spread":0.2079977813098972,"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."}}